SPEECHBUILDER Facilitating Spoken Dialogue System Development

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SpeechBuilder:Facilitating Spoken DialogueSystem DevelopmentbyEugene WeinsteinS.B.,Massachusetts Institute of Technology(2000)Submitted to the Department ofElectrical Engineering and Computer Sciencein partial fulﬁllment of the requirements for the degree ofMaster of Engineering inElectrical Engineering and Computer Scienceat theMASSACHUSETTS INSTITUTE OF TECHNOLOGYMay23,2001c Massachusetts Institute of Technology2001.All rights reserved. Author..............................................................Department ofElectrical Engineering and Computer ScienceMay23,2001 Certiﬁed by..........................................................James R.GlassPrincipal Research ScientistThesis Supervisor Accepted by.........................................................Arthur C.Smith Chairman,Department Committee on Graduate ThesesSpeechBuilder:Facilitating Spoken Dialogue SystemDevelopmentbyEugene WeinsteinSubmitted to the Department ofElectrical Engineering and Computer Scienceon May23,2001,in partial fulﬁllment of therequirements for the degree ofMaster of Engineering inElectrical Engineering and Computer ScienceAbstractSpeechBuilder is a suite of tools that helps facilitate the creation of mixed-initiative spoken dialogue systems for both novice and experienced developers of human lan-guage applications.SpeechBuilder employs intuitive methods of speciﬁcation to allow developers to create human language interfaces to structured information stored in a relational database,or to control-and transaction-based applications.The goal of this project has been both to robustly accommodate the various scenarios where spoken dialogue systems may be needed,and to provide a stable and reliable infras-tructure for design and deployment of applications.SpeechBuilder has been used in various spoken language domains,including a directory of the people working at the MIT Laboratory for Computer Science,an application to control the various physical items in a typical oﬃce environment,and a system for real-time weather information access.Thesis Supervisor:James R.GlassTitle:Principal Research ScientistAcknowledgmentsFirst,I would like to sincerely thank my thesis advisor,Jim Glass,for inspiring this project and working through it with me all the way to the completion to this thesis. Without his guidance,perseverance,and patience,I would have never been able to ﬁnish this work.In addition,I would like to thank all of the staﬀ,visiting scientists,and students that have helped out in this work.Just about every person in the Spoken Language Systems Group has contributed to creating SpeechBuilder,many of them making quite a signiﬁcant impact on the system.Of these people,I would like to speciﬁcally thank Issam Bazzi,Scott Cyphers,Ed Filisko,TJ Hazen,Lee Hetherington,Mikio Nakano,Joe Polifroni,Stephanie Seneﬀ,Lynn Shen,Chao Wang,and Jon Yi.I would also like to thank Jef Pearlman,who built SpeechBuilder’s predecessor system, SLS-Lite,as part of his Master’s thesis research.Finally,I would like to thank my family and friends.My father Alexander,mother Alla,and sister Ellen have all given me the encouragement and care without which I could not have made it through MIT.I am also very grateful to my friends for their support and encouragement,especially in the home stretch of this thesis.Speciﬁcally, I would like to thank my friend Jean Hsu for proofreading this paper.This research was supported by DARPA under contract N66001-99-1-8904monitored through Naval Command,Control and Ocean Surveillance Center and under an in-dustrial consortium supporting the MIT Oxygen Alliance.Contents1Introduction131.1Motivation (13)1.2Goals (14)1.3Approach (15)1.4Terminology (16)1.5Outline (17)2Background192.1SLS-Lite (19)2.2VoiceXML (20)2.3Tellme Studio (21)2.4SpeechWorks and Nuance (22)2.5Others (23)3Architecture253.1Technology Components (25)3.1.1Components Used Both in Control and Info Models (26)3.1.2Components used only in Info Model (32)3.1.3Component Used only in Control Model–Back-end (33)3.2Web Interface (34)3.2.1Keys and Actions (34)3.2.2Vocabulary Editing (34)3.2.3Log Viewing (35)4Knowledge Representation394.1Linguistic Concept Representation (39)4.1.1Concept Keys (40)4.1.2Sentence-level Actions (41)4.1.3Concept Hierarchy (41)4.2Data Concept Representation (42)4.3Response Generation (45)5Development Issues475.1Language Model Overgeneralization (47)5.1.1Problem Formulation (47)5.1.2Reducing Overgeneralization (49)5.2Hub Programs (50)5.3Creating Examples Based on Data (51)5.3.1Sentence-level Actions (51)5.3.2Responses (52)5.4Pronunciation Generation (52)5.5Registration (53)5.6User Domain Access (53)5.6.1Central Access Line (54)5.6.2Local Audio Deployment (55)5.7Echo Script (56)5.8Parse Tree Display (56)6Current Status576.1Fully-functional Domains (58)6.1.1LCSInfo and SLSInfo (58)6.1.2SSCPhone (59)6.1.3Oﬃce (59)6.1.4MACK (59)6.1.5Weather (60)6.2Trial Domains (61)6.2.1Schedule (61)6.2.2Stocks (61)6.2.3Flights (61)7Conclusions637.1Dialogue Control (64)7.2Communication Protocol (64)7.3Conﬁdence Scoring (65)7.4Unsupervised Training (65)7.5Synthesis (66)7.6Multi-lingual SpeechBuilder (66)7.6.1Japanese–JSpeechBuilder (66)7.6.2Mandarin Chinese–CSpeechBuilder (67)7.7Database Schema (67)List of Figures3-1Info model SpeechBuilder conﬁguration utilizing full set of galaxy components (26)3-2Control model SpeechBuilder conﬁguration utilizing limited set of galaxy components (27)3-3Semantic frame for query“What is the weather in Boston Massa-chusetts on Monday?” (29)3-4Semantic frame for query“Are there any flights leaving before ten a m?” (29)3-5tina parse tree for query“Are there any flights leaving before ten a m?” (30)3-6E-form for“What is the phone number for Victor Zue?” (31)3-7Web interface for SpeechBuilder.The screen shows editing example sentences for the“list”action (36)3-8The SpeechBuilder vocabulary editing tool.The screen shows the developer editing the pronunciations of two words (37)3-9Log viewing tool used by SpeechBuilder.The screen shows a de-veloper examining two utterances spoken by the user (38)4-1Generalized sentence template from the example sentence“Turn on the TV in the living room” (41)4-2Semantic frame for query“Turn on the TV in the living room”using generalized template shown in Figure4-1 (41)4-3Data for the“Info”domain as a CSVﬁle (43)4-4SpeechBuilder response editing tool (45)5-1Subset of a language parsing grammar that exhibits the overgeneral-ization problem(dollar signs indicate grammar terminal nodes) (49)5-2The grammar of Figure5-1rewritten so as to avoid the overgeneral-ization problem(dollar signs indicate grammar terminal nodes) (50)5-3The SpeechBuilder developer registration tool (54)List of Tables3.1Sample utterances and their CGI encodings made by the generationcomponent (31)4.1Examples of concept keys (40)4.2Examples of sentence-level actions (42)4.3Examples of strict(==)andﬂattened(=)hierarchy (43)4.4Data from the“info”domain (43)Chapter1IntroductionThe process of designing,implementing,and deploying a mixed-initiative spoken di-alogue system is non-trivial.Creating a competent and robust system that aﬀords its users signiﬁcant freedom in what they can say and when they can say it has al-ways been a diﬃcult task.Building these systems is time-consuming and tedious for those who can be considered experts in thisﬁeld,and essentially impossible for those without any experience.SpeechBuilder,which has been developed as part of this thesis research,is a suite of tools that simpliﬁes and streamlines this pro-cess.SpeechBuilder aims to provide methods of conﬁguring application speciﬁcs that even a complete novice developer can tackle,while maintaining the power and ﬂexibility that experienced developers desire.1.1MotivationAmong the many factors motivating the creation of SpeechBuilder,the most prominent were1)the beneﬁts of making spoken dialogue system development acces-sible to novices,2)the opportunities for data collection,and3)the need to provide an eﬃcient way for experts to rapidly build spoken dialogue systems.Over the past decade,the Spoken Language Systems Group at the MIT Labora-tory for Computer Science has been actively developing the human language technolo-gies necessary for creating conversational human-machine interfaces.In recent yearsseveral systems have been publicly deployed on toll-free telephone numbers in North America,including systems providing access to information about weather forecasts,ﬂight status,andﬂight schedules and prices[37,28].Although these applications have been successful,there are limited resources at MIT to develop a large number of new domains.Since it is the development of new domains that often exposes weaknesses or deﬁciencies in current technology,the scarcity of resources is a stumbling block for the advancement of the state of the art.SpeechBuilder attempts to address this problem by streamlining the application development and deployment process,while maintaining use of the same technologies that are used to build dialogue systems by hand.Even with tools such as SpeechBuilder,the variety of spoken dialogue systems that experts can propose and create is limited.However,the number of people that may be interested in creating applications using spoken language technology is vir-tually unlimited,as are their ideas.Thus,it is naturally desirable for users of all skill levels and backgrounds to be able to experiment with this technology.Speech-Builder addresses this problem by utilizing intuitive methods of speciﬁcation and easily-managed deployment schemes to allow novice users to build spoken dialogue systems.Lastly,enabling a wide range of developers to implement and deploy various novel domains presents signiﬁcant opportunities for data collection.Applications built using SpeechBuilder will provide raw data that will help improve the acoustic-phonetic models used in recognition at MIT.This is especially useful because it is possible that SpeechBuilder domains will present data from a wider variety of speakers and acoustic environments than that of the systems deployed to date.1.2GoalsIn this work the goals have been1)to robustly accommodate the various scenarios where spoken dialogue systems may be needed,and2)to provide a stable and reliable infrastructure for design and deployment of applications.Success in this project wouldbe to achieve these goals while maintaining ease of use for developers seeking to build domains.In past work dealing with facilitating spoken language system development[22], the predominant model for SpeechBuilder-type tools has been to provide a speech interface to a pre-existing application,the functionality of which appears as a black box to the human language technologies.However,over the course of developing SpeechBuilder it became clear that many domains that were of interest were in-formation access applications(e.g.weather,ﬂight information,stock quotes,direc-tions).Thus,it became desirable to develop a SpeechBuilder application model that involves conﬁguring a speech-driven front end to a set of structured informa-tion.SpeechBuilder addresses both problems in what have become two separate application development approaches and methodologies.SpeechBuilder attempts to enable developers to build conversational systems that are comparable in quality and robustness to hand-built systems.This eﬀort involves many factors,such as providing eﬀective mixed-initiative dialogue strategies, robust language models for recognition and natural language parsing grammars,and the tools for modeling the often complex relations between information concepts.1.3ApproachThe approach that has been used in SpeechBuilder is to leverage the basic tech-nologies that have been used to hand-build various demonstration dialogue sys-tems within the galaxy framework for spoken language system development[27]. SpeechBuilder uses information speciﬁed by the developer to conﬁgure human language technology(HLT)components based on the speciﬁcations and constraints provided by the developer.Since SpeechBuilder aims to allow developers to create spoken dialogue systems comparable to those that have been hand-built in the past, it is natural that SpeechBuilder should utilize much of the same human language technology framework as the hand-built domains.In addition to this intuitive con-nection between SpeechBuilder domains and these technology components,thereare several other reasons why this approach has been selected.First,signiﬁcant eﬀort has been devoted in the past at MIT to improving tech-nology in dialogue system architecture[27],speech recognition[11],language un-derstanding[24],language generation[1],discourse and dialogue[28],and,most recently speech synthesis[36].Employing these HLT components minimizes du-plication of eﬀort,and maximizes SpeechBuilder’sﬂexibility to adopt technical advances made in these areas,which may be achieved in eﬀorts entirely disjoint from SpeechBuilder development.Second,since SpeechBuilder uses the full and unconstrained set of technology components,domains created using SpeechBuilder can eventually scale up to the same level of sophistication as that of the domains that have been hand-built at MIT. In fact,this enables expert developers to use SpeechBuilder as a“springboard”to bypass the often tedious and stressful eﬀort of going through the initial stages of spoken language application development.Finally,since SpeechBuilder conﬁgures the core HLT components to accommo-date user constraints and speciﬁcations,it is quite likely that the unforeseen scenarios created by developers building various domains will expose deﬁciencies in the HLT’s and thus drive potential improvements.In addition,since SpeechBuilder aims to build portable domains with independently functioning components,it can help stimulate improvements to the abstraction and portability of these components.1.4TerminologyAs mentioned in Section1.2,SpeechBuilder is modeled to allow for creation of applications in the various scenarios for which speech-driven applications may be needed.Currently,SpeechBuilder has two fundamentally diﬀerent models for domain structure to accommodate these scenarios.Theﬁrst model assumes that the developer desires to“speech-enable”an appli-cation;that is,there is an application in existence,and the developer would like to create a spoken language interface to that application.This is referred to as thecontrol model,because it can be used to create applications controlling various real-world devices or software extensions.An example of an application that can be implemented via the control model is the oﬃce device control domain,which allows a person to use speech to control the various physical devices in their oﬃce.Another example is a domain allowing the booking ofﬂight reservations using spoken dia-logue–this falls under the control model because of the complex transaction control involved in completing a request.The second model is designed for developers who want to provide a spoken in-terface to structured information stored in a database.This is referred to as the info model.Examples of domains that fall under this model include stock quotes, weather information,schedule access,and radio station information.1.5OutlineChapter2discusses previous work on facilitating the design and development of spoken dialogue systems.Previously implemented technologies are compared and contrasted to SpeechBuilder.Chapter3gives an overview of SpeechBuilder’s architecture.It discusses how the various human language technology components are used within SpeechBuilder in both the control and the info models.This chapter also describes the web interface that developers use to conﬁgure domain speciﬁcs.Chapter4describes the knowledge representation used by SpeechBuilder.It addresses the key-action linguistic concept representation inherited from the SLS-Lite system,the data concept representation used in the info model,and the response speciﬁcation facility used in SpeechBuilder.Chapter5discusses some of the more non-trivial issues that have come up during SpeechBuilder development and implementation.Speciﬁcally,it discusses issues in speech recognition and natural language understanding,example generation,text-to-phone conversion,and overall infrastructure.Chapter6describes the current operating status of SpeechBuilder and givesan overview of some of the domains that have been built by various developers inside and outside of MIT.Chapter7summarizes the thesis and proposes some future work that would im-prove SpeechBuilder’s infrastructure and robustness.Chapter2BackgroundThere are a number of eﬀorts,present both in industry and in academia,to provide tools for development of systems using spoken language and the formalisms for deﬁn-ing such systems.SpeechBuilder and its predecessor system,SLS-Lite,appear to be among a small set of toolkits for development of mixed-initiative natural language environments.This chapter presents the eﬀorts to date and compares them to the work done in this thesis.2.1SLS-LiteSLS-Lite is the prototype system that preceded SpeechBuilder[22].Speech-Builder inherits much of its philosophy,as well as many of its features(such as the linguistic concept representation),from SLS-Lite.The SLS-Lite system focused on building spoken language interfaces to appli-cations that might normally be driven with other modalities.SLS-Lite was the ﬁrst implementation of the framework linking recognition and understanding compo-nents to a back-end application that implements domain-speciﬁc functionality and turn management.This framework is still present as the“control model”in Speech-Builder.SLS-Lite introduced the methodology of allowing developers to conﬁgure do-main speciﬁcs using intuitive methods of speciﬁcation.Developers speciﬁed linguisticconstraints and natural language templates by giving example user queries(actions) and identifying concepts(keys).This is described in more detail in Chapter4.Finally,much of the code base for the SLS-Lite system is present in Speech-Builder.SpeechBuilder introduces a more sound deployment infrastructure,and a new mode of operation(info model)utilizing all of the major galaxy components. However,SLS-Lite is the foundation framework on which SpeechBuilder was cre-ated,therefore much credit for the work leading up to this thesis research should be given to Jef Pearlman and Jim Glass,who designed and implemented the original SLS-Lite system[22].2.2VoiceXMLVoiceXML,the Voice eXtensible Markup Language,is a language for describing human-machine interactions utilizing speech recognition,natural language under-standing,and speech synthesis.VoiceXML was developed by a forum consisting of various companies interested in spoken dialogue systems(such as AT&T,IBM, Lucent and Motorola)and was later standardized by the World Wide Web Con-sortium[33].VoiceXML seeks to provide a medium for streamlined speech-driven application development.However,the philosophy of VoiceXML diﬀers from that of SpeechBuilder in several key areas.Primarily,SpeechBuilder is orthogonal in purpose to VoiceXML.VoiceXML is a mark-up language which allows developers to specify the dialogueﬂow and the language processing speciﬁcs of a speech-driven application.SpeechBuilder is more of an abstraction layer between the developers of spoken language domains and the technology that implements these domains.In fact,it is entirely possible that in the future,SpeechBuilder will able to generate VoiceXML representations of the domains built by developers.However,at the current time,SpeechBuilder development has focused around deploying domains in the galaxy framework[27] (galaxy has been adopted by the DARPA Communicator Program as the reference architecture for design of mixed-initiative spoken language domains[26]).In contrast,the VoiceXML standard is focused on more directed-dialogue applications,using very constrained language parsing grammars.Since moreﬂexible mixed-initiative systems are harder to create than directed-dialogue domains,there is more of a need for tools such as SpeechBuilder to help developers accomplish this task.While SpeechBuilder is orthogonal to VoiceXML in purpose,it is important to compare the technologies currently used by SpeechBuilder to those present in systems speciﬁed with VoiceXML.VoiceXML uses Java Speech Grammar Format (JSGF)[14]context-free grammars to deﬁne natural language understanding tem-plates,with mechanisms for parsing poorly formulated queries.SpeechBuilder domain recognizers are currently conﬁgured to use a hierarchical n-gram language modeling mechanism(a more robust model for multi-concept queries)with backoﬀto robust parsing(to help identify concepts when the user’s utterance contains use-ful information but does not match the parsing grammars exactly).In addition,as mentioned above,VoiceXML is primarily a protocol for describing human-machine interactions in a predeﬁnedﬁnite-state sequence of events(this is known as“directed dialogue”).In contrast,SpeechBuilder allows developers to build mixed-initiative applications,where the user can say any in-domain utterance at any time in the conversation.Finally,since VoiceXML is a speciﬁcation language and not a complete system for application conﬁguration and deployment such as SpeechBuilder,an implementa-tion utilizing VoiceXML requires the conﬁguration of third-party speech recognition and synthesis components.Several groups have implemented systems based on the VoiceXML framework.It is appropriate to compare the functionality andﬂexibility of these systems to that of SpeechBuilder,and this is done in the next two sections of this chapter.2.3Tellme StudioTellme Studio is one implementation of the VoiceXML framework.It is similar to SpeechBuilder in that it uses a web interface to conﬁgure application speciﬁcs.However,it diﬀers fundamentally from SpeechBuilder in that it requires develop-ers to write JSGF grammars and callﬂow automata as part of the VoiceXML doc-ument deﬁning their domains.Thus,using Tellme Studio requires a certain amount of expertise,and can involve a signiﬁcant learning curve for those developers not fa-miliar with writing language parsing grammars.SpeechBuilder’s web interface, in contrast,uses example-based speciﬁcation methods to deﬁne grammars and other domain speciﬁcs.In fact,any developer can conﬁgure all of the aspects of the appli-cation using the web interface;while more experienced developers are able to modify domain speciﬁcs by manipulating the raw XML representation of their domains.SpeechBuilder is similar to Tellme Studio in that it allows developers to de-ploy applications using the the respective development site’s HLT framework and telephony infrastructure.In addition,both systems allow for local installations for advanced developers who would like to migrate mature applications to a deployment infrastructure at the developer’s actual physical location.2.4SpeechWorks and NuanceSpeechWorks[32]and Nuance[21],two of the market leaders in spoken dialogue system development,have both begun eﬀorts to simplify development of voice-driven systems for their customers.SpeechWorks has created the SpeechSite product,which is a pre-packaged solution for company information retrieval and presentation using a speech-driven interface.SpeechSite is available commercially at a price level primarily accessible to medium-to large-size companies.SpeechSite is similar in purpose to some applications that may be built using SpeechBuilder,but is designed for a speciﬁc domain and therefore can not be directly compared to SpeechBuilder.In addition,SpeechWorks has implemented a“VoiceXML browser,”which is a software package which deploys realizations of VoiceXML domains within the SpeechWorks speech recognition and synthesis framework.Nuance has developed V-Builder,which is a commercial product designed for im-plementing VoiceXML-speciﬁed speech-driven domains.V-Builder provides a graphi-cal user interface to allow developers to build applications compliant with the VoiceXML protocol.However,domains created using V-Builder are prone to the same directed dialogue callﬂow and grammar limitations as Tellme Studio(see section2.3).2.5OthersPhilips has created a uniﬁed platform for speech application development called SpeechMania[23].SpeechMania allows developers to deﬁne spoken language appli-cations using a language called High Level Dialogue Description Language(HDDL). HDDL gives the developers total control over theﬂow of the dialogue,and provides connections to language technology components such as speech recognition,natural language parsing,and synthesis.In this,SpeechMania is very similar to Speech-Builder(in the control model).In contrast to SpeechBuilder,SpeechMania does not utilize intuitive methods of linguistic speciﬁcation,and therefore is not as acces-sible to novice developers and those looking to quickly create a working application. However,it is probably a more robust solution than SpeechBuilder for experienced developers of dialogue systems who want to have total control over the dialogue and HLT components.Because SpeechMania allows the developer to control the callﬂow using HDDL,it is not restricted to directed dialogue as VoiceXML systems are.The Center for Spoken Language Understanding(CSLU)at the Oregon Graduate Institute of Science and Technology has created a toolkit for the design of spoken dialogue systems[6].The CSLU Toolkit utilizes example-based methods of specify-ing natural language understanding and generation templates,and uses a graphical interface to conﬁgure the dialogue manager.These features make the CSLU Toolkit accessible to developers of all levels of expertise;however,it is still constrained to building only directed-dialogue systems.Chapter3ArchitectureSpeechBuilder makes use of the galaxy framework[27],which is used by all of the spoken dialogue systems developed at MIT(see Section1.3).galaxy is a highlyﬂexible and scalable architecture for dialogue systems,and it serves as the DARPA reference architecture for building research systems utilizing human language technologies[26].Theﬁrst section of this chapter describes the HLT components that are used within SpeechBuilder and how the galaxy framework is used to unify them.The second section describes the web-based interface that the developer uses to conﬁgure domains.3.1Technology ComponentsThe technology components for SpeechBuilder domains created within the control model are conﬁgured quite diﬀerently from those using the info model(Section1.4 deﬁnes these terms).Speciﬁcally,control model domains use a very limited language generation server and have no built-in components to manage dialogue,discourse, or information model domains utilize a more complete set of HLT components,similar to dialogue systems previously built within the galaxy frame-work[37,28].However,several components are present in both kinds of domains, and are conﬁgured largely as they are in SLS-Lite[22],the predecessor system to SpeechBuilder.This section presents the major technology components organizedby which domain model(s)use them,and ordered sequentially as they are used dur-ing a turn of an application.Figures3-1and3-2illustrate the HLT architecture of a SpeechBuilder domain,in the info model and the control model,respectively.Figure3-1:Info model SpeechBuilder conﬁguration utilizing full set of galaxy components3.1.1Components Used Both in Control and Info Models HubThe galaxy hub manages the communication between the HLT components in the application.While some galaxy components may be designed to operate directly on other components’output(e.g.the natural language understanding component takes in N-best hypotheses from the recognizer),each component is written so that it operates completely independently of the rest of the system.Thus,a central con-trol module is necessary to control the informationﬂow between the various HLT components.The hub serves as this module.The hub is a programmable server which uses a rule-based language to specify。

VoiceBuilder A Framework for Automatic Speech Application Development

VoiceBuilder:A Framework for Automatic Speech Application Development Heriberto Cuay´a huitl,Miguel´Angel Rodr´ıguez-Moreno,and Juventino Montiel-Hern´a ndezUniversidad Aut´o noma de TlaxcalaDepartment of Engineering and TechnologyIntelligent Systems Research GroupApartado Postal#140,Apizaco,Tlaxcala,90300,Mexicohcuayahu@ingenieria.uatx.mx,ne0ph0enix@,jmontiel@ingenieria.uatx.mxAbstractIn this paper we present V oiceBuilder,a framework for automat-ing the process of developing speech applications.Our framework allows speech User Interface(UI)specialists to introduce UI’s in two ways:a stand-alone GUI application,and a web-based inter-face;in which speech UI’s are stored in a markup language previ-ously proposed called SUIML[1],supporting either system initia-tive or mixed initiative dialogue strategies.For automatic coding, we propose an algorithm based on a macro-processor that generates V oiceXML code by parsing SUIML documents.This algorithm was designed to generate various kinds of code with a minimal initial ef-fort.We performed experiments considering both system initiative and mixed initiative dialogue strategies with three different speech applications:auto-attendant,e-mail reader,andﬂight reservations. V oiceBuilder is very useful for building speech applications in new domains,requires no programming effort and could be incorporated into several voice toolkits.1.IntroductionOur information-based society has created a large demand for the development of speech applications.Currently,expert programmers perform speech application development once a speech User Inter-face(UI)speciﬁcation is written.However,the development phase may require re-recoding in the following scenarios:because of un-satisﬁed customers who request changes in the UI,because usability testing(usually performed beforeﬁnishing coding)might require changes in the UI,and because adding functionality after deployed applications might affect part of the coded UI.Furthermore,mixed initiative spoken dialogue systems are more difﬁcult to implement than system initiative systems.These scenarios illustrate the fact that a mechanism for coding automatically from a UI document could dramatically reduce the time and effort spent in the speech application development process.In the past,few research efforts have been undertaken in this area.For instance,the CLSU Toolkit provides a rapid application development approach[2],but still a lot of visual programming work must be performed,once the UI has been deﬁned.Current frameworks such as SPEECHBUILDER allows developers to specify linguistic information and rapidly cre-ate mixed initiative spoken dialogue systems[3],DialogXML ex-presses the notion of states and transitions automatically compiled to V oiceXML[4],and the Universal Speech Interface(USI)project enables the automatic generation of new interfaces from a high-level speciﬁcation[5].Other current approaches consist of making deci-sions dynamically on dialogueﬂow based on analysis of data[6]. Despite of the recent work on the mixed initiative dialogue strategy, we must continue developing automatic or semi-automatic methods for facilitating development of conversational systems[7].Figure1:Architecture of VoiceBuilder.In this research we propose an approach for automatic speech application development based on SUIML documents introduced from both web-based or stand-alone interfaces and generating V oiceXML code through a proposed algorithm.In the remainder of this paper,weﬁrst provide the architecture of V oiceBuilder in section2.We then describe SUIML(our markup language that speciﬁes conversations)in section3.In section4we describe the code generator,SAC(Speech Application Coder).In section5we describe experiments with three different applications.Finally,in section6we provide some conclusions and future directions.2.Architecture of VoiceBuilder Because current development environments require signiﬁcant ef-fort in visual programming for producing speech applications,our approach for dealing with automatic speech application develop-ment consists in automatic code generation based on a UI doc-ument.Figure1shows the architecture of V oiceBuilder,where through a web-based interface or stand-alone GUI application the UI specialist writes the speech UI speciﬁcation,stored in a SUIML (Speech User Interface Markup Language)document,which is then given as input to the code generator.The resulting V oiceXML code may set/get content from a database,and includes a dialogue man-ager using a Finite State Automaton(FSA)for handling transitions among the conversation.V oiceBuilder uses web technologies for both capturing the UI and for generating code.3.Speech User Interface Markup Language SUIML(Speech User Interface Markup Language)is an eXtended Markup Language(XML)application[8]that speciﬁes conversa-tions between man and machine[1].A SUIML document describes a set of objects that represent almost all the information needed in a conversation except for grammars.In a SUIML document(See following fragment)the root element can contain only one instance of each of its four child elements:comment,globals,dialogueman-ager,and conversation.The comment element is used to describe the speech application.The globals element contains information inherited by the speechcollection element,due to the fact that much information is repeated.The dialoguemanager element speciﬁes an FSA used to control theﬂow of the conversation.The conversation element speciﬁes the components of a conversation(the states of the FSA)through N instances of the elements:playprompt,speech-collection,databasequery and mixedinitiative.The playprompt el-ement contains information to play prompts and pre-recorded mes-sages.The speechcollection element contains information for play-ing messages in order to request a speech input,given in terms of a vocabulary or grammar;the childs speciﬁed in globals can be rewriten in this element.The databasequery element contains in-formation for querying a database system,allowing either select or insert operations.Finally,the mixedinitiative element supports complex dialogues,allowing natural language utterances embed-ding several keywords and reusing the speechcollection element.<suiml name="Air Mexico"><comment>Voice portal for Air Mexico</comment><globals><commands><command wording="main menu"goto="q1"/><command wording="good bye"goto="q18"/></commands><timeouts><timeout>I’m sorry,I didn’t hear you.</timeout><timeout>I’m sorry,I still don’t hear you.</timeout></timeouts><retries><retry>I’m sorry,I didn’t understand.</retry><retry>I’m sorry,I still don’t understand.</retry></retries><confirmation default="always"><prefix>I think you said</prefix><suffix>is that right?</suffix></confirmation><confidence lowthreshold="40"highthreshold="50"/> </globals><dialoguemanager><states><state id="q0">Welcome</state><state id="q1">Main_Menu</state><state id="q2">Ask_Name</state><state id="q3">Validate_Person Name</state><state id="q4">Ask_Destination</state>...</states><startstate><state>q0</state></startstate><finalstates><state>q14</state><state>q18</state></finalstates><transitionfunction><input state="q0"condition="epsilon">q1</input><input state="q1"condition="grammar"value="main_menu.gram"slots="nip">q2</input>...<input state="q4"condition="grammar"value="destination.gram"slots="city,date,time">q5</input>...</transitionfunction></dialoguemanager><conversation><playprompt name="Welcome"bargein="false"><prompts-pp><prompt>Welcome to Air Mexico.</prompt><sound file="intro.wav"/></prompts-pp></playprompt><speechcollection name="Main_Menu"bargein="false"> <prompts-sc><initial>Main Menu!If you want to make aflight reservation please say your personalidentification number;otherwise you can sayflight status,hotels,weather,or currencyexchange.</initial><help>If you want to make a flight reservationplease say the four digits of your personalidentification number;otherwise for consultinginformation say flight status,hotels,weather,or currency exchange.</help></prompts-sc><confirmation default="ifnecessary"/><confidence lowthreshold="40"highthreshold="48"/> </speechcollection><databasequery name="Retrieve_Flights"operation="select"><inputfields>3</inputfields><query>SELECT airline,time FROM flights WHEREcity=field1and date=field2and time=field3</query> </databasequery><mixedinitiative name="Ask_Destination"><speechcollection name="Destination"bargein="false"><prompts-sc><initial>Please tell me where would you liketo go?</initial><help>For example,you can say"I would like toflight to Toronto next Sunday morning!"</help> </prompts-sc></speechcollection><components><speechcollection name="Destination_City"bargein="false">...</speechcollection><speechcollection name="Destination_Date"bargein="false">...</speechcollection><speechcollection name="Destination_Time"bargein="false">...</speechcollection></components></mixedinitiative>...</conversation></suiml>3.1.Stand-Alone GUI ApplicationV oiceBuilder employs a stand-alone GUI application for introduc-ing UI speciﬁcations,implemented in Java,in which two main screens are the work area:the conﬁgurations screen and the UI speciﬁcation screen.Theﬁrst screen is conﬁgured with a descrip-tion of the application,the globals,and the database.The second screen uses drag-and-drop capabilities,for introducing components that deﬁne the conversation,which must be linked to specify the controlﬂow.In addition,each component in the UI(playprompt, speechcollection,databasequery or mixedinitiative)must be conﬁg-ured with its corresponding information.Finally,the UI speciﬁca-tions are saved or loaded using the SUIML language.3.2.Web-Based InterfaceV oiceBuilder also employs a web-based interface for introducing UI speciﬁcations,implemented in PHP,using a work area simi-lar to that of the GUI but with hypertext and HTML components. The initial web-page provides components for the same conﬁgura-tions speciﬁed in the GUI interface.The second main web-page allows the introduction of components that make up the conver-sation.In this web-page the components and their corresponding names are added,forming a list through which hyperlinks link to new web-pages for conﬁguring each component.UI speciﬁcations can be saved or loaded using SUIML.V oiceBuilder employs both interfaces in order to provide a choice to UI specialists,and to per-form further experiments,so that specialists might introduce UIs ina more comfortable interface.Figure2:Data structures for the SAC algorithm.4.The Code GeneratorThe code generation algorithm,SAC(Speech Application Coder), which receives SUIML documents as input and produces V oiceXML code[9],was inspired by the macro-processor algorithm deﬁned by Leland Beck in[10].This algorithm performs two main steps:theﬁrst step initializes the data structures,and the second step produces an expandedﬁle in V oiceXML using the values of the data structures.The algorithm involves four main data struc-tures:NAMTAB,DEFTAB,ARGTAB,andδ.The macro1names are entered into NAMTAB,which contains pointers to the begin-ning and end of the deﬁnition of DEFTAB.Figure2shows how pointers of NAMTAB refer to the corresponding macro deﬁnition in DEFTAB.The macro deﬁnitions are stored in DEFTAB,which con-tains the macro prototype and the macro body.Both NAMTAB and DEFTAB are initialized from a textﬁle with the form of DEFTAB except for the line numbers.When a macro invocation is recognized (when parsing the SUIML document),the arguments are stored in ARGTAB according to their position in the argument list.(See Fig-ure2,line11of DEFTAB and contents of ARGTAB)The fourth data structure isδ(the transition matrix of the FSA),which is used for setting the value of the argument(s)goto in ARGTAB.Following the SAC algorithm(seeﬁgure3),two inputs are needed:the code templatesﬁle(macros)and the speech user inter-face speciﬁcation(UI)in SUIML format.Then,data structures are declared and cleared and the function LOAD-MACROS is invoked in order to initialize NAMTAB and DEFTAB,which store the code templates.The main loop in the algorithm reads over the SUIML document and,if the element“dialoguemanager”is found,the func-tion LOAD-FSA is invoked in order to build the transition ma-trix;otherwise,for each element found in NAMTAB,the EXPAND function is invoked in order to generate functional V oiceXML code, passing as an argument the corresponding subtree.1We will refer to the code generation process as the expansion of code performed by a macro-processor.Thus,for the purpose of explanation we will refer to the code templates in V oiceXML as macros.algorithm SAC(File macros,SUIML ui)input:A textﬁle containing code templates in V oiceXML and an XMLﬁle containing the speech UI speciﬁcation using the SUIML language. output:An expandedﬁle in V oiceXML.declare and clear data structures NAMTAB,DEFTAB,ARGTAB,andδLOAD-MACROS(macros)while not EOF ui doread next line from inputﬁlesearch TAGNAME in NAMTABif found thenEXPAND(subtree of current element)else if TAGNAME=“dialoguemanager”thenLOAD-FSA(subtree of current element)end ifend whileendfunction EXPAND(SUIML subtree)prototype←macro deﬁnition prototype from DEFTABLOAD-ARGS(subtree,prototype)write prototype to expandedﬁle as a commentwhile not end of macro deﬁnition doget next line of macro deﬁnition from DEFTABsubstitute arguments from ARGTAB for positional notationif line contains positional notation and is not empty thenwrite line to expandedﬁleend ifend whileendfunction LOAD-MACROS(File macros)while not EOF macros doif start macro deﬁnition thenenter element name into NAMTABenter begin pointer into NAMTABenter prototype deﬁnition into DEFTABelse if end macro deﬁnition thenenter end pointer into NAMTABelseenter line into DEFTABend ifend whileendfunction LOAD-FSA(SUIML subtree)for each element in the subtree doif TAGNAME=“states”thenQ←the set of states←the alphabet of symbols(obtained from the attributes of the“input”elements) else if TAGNAME=“startstate”thencurrentstate←q0←the start stateelse if TAGNAME=“ﬁnalstates”thenF←the set ofﬁnal stateselse if TAGNAME=”transitionfunction”thenδ←the transition matrixend ifend forendfunction LOAD-ARGS(SUIML subtree,String prototype)clear ARGTABfor each element in the subtree doget arguments and content from elementset values in ARGTAB according to index in prototype end forcurrentstate←get state based on the name of the current elementfor each key-value pair{state,symbol}with non-null valueattribute goto in ARGTAB←δ(currentstate,symbol) end forendFigure3:SAC:The Code Generation Algorithm.The EXPAND function starts invoking the function LOAD-ARGS,which set values in ARGTAB according to the macro pro-totype deﬁnition(Note that this table is cleared and initialized for every macro invocation.)Once all data structures have information, for each line in the macro body obtained from DEFTAB,positional notation is substituted with the argument values.If positional no-tation has no value this line is not considered part of the expanded ﬁle;otherwise,the line is written to the expandedﬁle.An iterative process is performed for each element within the SUIML document found in NAMTAB.In summary,theﬁrst step initializes NAMTAB, DEFTAB andδ;and the second step expands each element within the SUIML document,which is a subtree in XML form,to its corre-sponding code template found in DEFTAB using the arguments set up in ARGTAB.The SAC algorithm ofﬁgure3was implemented in Java and PHP using DOM[11]for parsing SUIML documents.5.Experiments and ResultsWe performed experiments with three different speech applications in the domains of auto-attendant,e-mail reader andﬂight reserva-tions;using both dialogue strategies:system initiative,and mixed initiative.These experiments were performed using two user inter-faces for introducing speech UIs:a stand-alone GUI application, and a web-based interface.We observed that both interfaces offer advantages and disadvantages.For instance,big speech applica-tions using the GUI interface are difﬁcult to handle,and although the web-based interface does not have fancy graphics,it can handle both small and big applications.The experiments were performed only by the authors of this paper based on UI speciﬁcations previ-ously written in a word processor containing tables holding infor-mation for each component in the conversation.After introducing the UI speciﬁcations in both interfaces,fully functional V oiceXML code was generated.For testing the speech applications,we used as a development environment IBM’s WebSphere Studio Application Developer version5,V oice Toolkit for WebSphere version4.2,and MySQL as DBMS.6.Conclusions and Future WorkIn this paper we have presented a framework for building speech applications with no programming effort,based on a markup lan-guage and an algorithm using code templates.The markup language (SUIML)was very useful for specifying conversations between man and machine and was tested for storing speech UI’s introduced by a speech UI specialist using two interfaces:a stand-alone GUI application and a web-based interface;where both seem to have advantages and disadvantages.We believe that using SUIML docu-ments provides several advantages as an intermediate language than generating V oiceXML directly from the web-based interface:con-versations speciﬁed in a high-level and more declarative language, other kinds of code might be generated,and because might specify a dynamic dialogue manager and then automatically generate code. The proposed algorithm(SAC)demonstrated automatic code gen-eration using SUIML documents and code templates in V oiceXML. The algorithm was inspired by the process followed by a macro-processor used for expanding macros in programs,which can be used as basis for generating other kinds of code,allowing SUIML in principle to act as a portable UI language across speech plat-forms.In addition,the approach proposed was able to generate fully functional speech applications using both system initiative and mixed initiative dialogue strategies.Thus,V oiceBuilder can be very useful for building spoken dialogue systems in new domains with no programming effort and could be easily incorporated into sev-eral voice toolkits.The goals of the framework are twofold:1)to serve as a testbed for performing research in the area of spoken dialogue systems in order to incorporate other either automatic or semi-automatic methods,in an attempt to contribute to the portabil-ity problem[7],and2)to save time and effort spent in the speech application development process so that more services might be au-tomated in the world.Suggestions for further research include,automatic grammar construction,dialogue management withﬂexible dialogues consid-ering users experience,the incorporation of this framework into a voice toolkit,code generation for other speech platforms,incorpo-ration of the retrieval of content from the web,interaction with nat-ural language processing components,and experiments developing speech applications in other domains,where SUIML might evolve for such new scenarios.Finally,we plan to offer V oiceBuilder freely available for educational and research purposes under request(for more information,send email to hcuayahu@ingenieria.uatx.mx).7.AcknowledgementsWe would like to thank Ben Serridge for his comments and writing revision on this paper.8.References[1]Montiel-Hern´a ndez,J.and Cuay´a huitl,H.,“SUIML:AMarkup Language for Facilitating Automatic Speech Appli-cation Development,”in proceedings of the MICAI’04(WIC), Mexico City,Mexico,Apr2004.[2]Sutton,S.,Cole,R.,Villiers,J.,Schalkwyk,J.,Vermeulen,P.,Macon,M.,Yan,Y.,Kaiser,E.,Rundle,B.,Shobaki,K.,Ho-som,P.,Kain,A.,Wouters,D.,Massaro,D.,and Cohen,M.,“Universal Speech Tools:The CSLU Toolkit,”in Proceedings of ICSLP,Sydney,Australia,Nov1998.[3]Glass,J.,Weinstein,E.,Cyphers,S.,Polifroni,J.,Chung,G.,and Nakano,N.,“A Framework for Developing Conver-sational User Interfaces,”in Proceedings of CADUI,Funchal, Isle of Madeira,Portugal,Jan2004.[4]Nyberg, E.,Mitamura,T,Placeway,P.,Duggan,M.,andHataoka,N.,“DialogXML:Extending V oiceXML for Dy-namic Dialog Management,”in proceedings of HLT,San Diego,USA,2002.[5]Toth,A.,Harris,T.,Sanders,J.,Shiver,S.,and Rosenfeld,R.,“Towards Every-Citizen’s Speech Interface:An Application Generator for Speech Interfaces to Databases,”in proceedings of ICSLP,Colorado,USA,Sep2002.[6]Polifroni,J.,Chung,G.,and Seneff,S.,“Towards the Auto-matic Generation of Mixed-Initiative Dialogue Systems from web Content,”in proceedings of EUROSPEECH,Geneva, Italy,Sep2003.[7]Zue,V.and Glass,J.,“Conversational Interfaces:Advancesand Challenges,”in proceedings of the IEEE,V ol.88,No.8, Aug2000.[8]W3C:Extensible Markup Language(XML) 1.0(ThirdEdition),Feb2004./TR/2004/REC-xml-20040204/[9]W3C:V oice Extensible Markup Language(V oiceXML)Ver-sion2.0,Mar2004./TR/voicexml20/ [10]Beck,L.L.,“System Software,An Introduction to SystemsProgramming,”Addison Wesley Longman,3rd Edition,1997, ISBN0-201-42300-6.[11]W3C:Document Object Model(DOM)/DOM/。

口才演讲比赛技巧文档

2020口才演讲比赛技巧文档SPEECH DRAFT口才演讲比赛技巧文档前言语料：温馨提醒，演讲又叫讲演或演说，是指在公众场合，以有声语言为主要手段，以体态语言为辅助手段，针对某个具体问题，鲜明、完整地发表自己的见解和主张，阐明事理或抒发情感，进行宣传鼓动的一种语言交际活动。

魅力演讲可以让演说者能够更好的抓住核心，把握本质，从根本上解决问题！把演讲的效果，发挥到极致，充分展现演讲魅力，释放能量，从而产生最大影响力！本文内容如下：【下载该文档后使用Word打开】口才演讲比赛加分技巧一、理解透彻的规则不同的比赛，有不同的关注点、不同的要求、不同的目的、不同的价值观，都会体现在大赛说明和评分规则中。

读透规则是参赛的首要前提，必须逐条研读，规则是参赛的指南针。

口才演讲比赛加分技巧二、紧扣主题的讲稿再好的主题，如果不是大赛所要求的，就是浪费。

见到了太多资质过人的选手都折在讲稿上，让人忍不住扼腕叹息。

讲稿一定要扣题，再根据个人风格和特长，决定讲稿的叙议情理。

口才演讲比赛加分技巧三、滚瓜烂熟的内容讲稿不熟，忘词或卡壳一定会扣分。

更重要的是，人的精力有限，如果现场要分神去回想要讲的内容，就不能全情投入去演绎，观众会感觉得到“像背诵”。

口才演讲比赛加分技巧四、恰到好处的时间一般超时或不足时都会扣分，在规定时间内30秒左右完成比较合适，还可以精确到提前5—10秒，甚至有的演讲高手可以精确到一秒不差。

口才演讲比赛加分技巧五、画龙点睛的道具善用道具者易得高分，古人云：“工欲善其事，必先利其器”，大赛更是如此。

口才演讲比赛加分技巧六、自信从容的心态心态影响状态，也影响发挥。

心态不好会完成过度紧张，因此参赛选手的心理素质也是能否得奖的决定因素之一。

良好的心理素质可以训练。

口才演讲比赛加分技巧七、合乎时宜的形象形象是评委们第一印象的重要组成部分，也直接决定了评委主观意识是加分还是减分。

形象最重要的是跟场合、跟主题、跟个人相匹配，才合乎时宜。

中英文献翻译：语音识别speech recognition

中英文献翻译：语音识别speech recognition Speech RecognitionVictor Zue, Ron Cole, & Wayne WardMIT Laboratory for Computer Science, Cambridge, Massachusetts, USA Oregon Graduate Institute of Science & Technology, Portland, Oregon, USACarnegie Mellon University, Pittsburgh, Pennsylvania, USA1 Defining the ProblemSpeech recognition is the process of converting an acoustic signal, captured by a microphone or a telephone, to a set of words. The recognized words can be the final results, as for applications such as commands & control, data entry, and document preparation. They can also serve as the input to further linguistic processing in order to achieve speech understanding, a subject covered in section.Speech recognition systems can be characterized by many parameters, some of the more important of which are shown in Figure. An isolated-word speech recognition system requires that the speaker pause briefly between words, whereas a continuous speech recognition system does not. Spontaneous, or extemporaneously generated, speech contains disfluencies, and is much more difficult to recognize than speech read from script. Some systems require speaker enrollment---a user must provide samples of his or her speech before using them, whereas other systems are said tobe speaker-independent, in that no enrollment is necessary. Some of theother parameters depend on the specific task. Recognition is generally more difficult when vocabularies are large or have many similar-sounding words. When speech is produced in a sequence of words, language models or artificial grammars are used to restrict the combination of words.The simplest language model can be specified as a finite-state network, where the permissible words following each word are given explicitly. More general language models approximating natural language are specified in terms of a context-sensitive grammar.1One popular measure of the difficulty of the task, combining the vocabulary size and the language model, is perplexity, loosely defined as the geometric mean of the number of words that can follow a wordafter the language model has been applied (see section for a discussion of language modeling in general and perplexity in particular). Finally, there are some external parameters that can affect speech recognition system performance, including the characteristics of the environmental noise and the type and the placement of the microphone.Parameters RangeSpeaking Mode Isolated words to continuous speechSpeaking Style Read speech to spontaneous speechEnrollment Speaker-dependent to Speaker-independentVocabulary Small(<20 words) to large(>20,000 words)Language Model Finite-state to context-sensitivePerplexity Small(<10) to large(>100)SNR High (>30 dB) to law (<10dB)Transducer Voice-cancelling microphone to telephoneTable: Typical parameters used to characterize the capability of speech recognition systemsSpeech recognition is a difficult problem, largely because of the many sources of variability associated with the signal. First, the acoustic realizations of phonemes, the smallest sound units of which words are composed, are highly dependent on the context in which they appear. These phonetic variabilities are exemplified by the acoustic differences of the phoneme，At wordboundaries, contextual variations can be quite dramatic---making gas shortage sound like gash shortage in American English, and devo andare sound like devandare in Italian.Second, acoustic variabilities can result from changes in the environment as well as in the position and characteristics of the transducer. Third, within-speaker variabilities can result from changes in the speaker's physical and emotional state, speaking rate, or voice quality. Finally, differences in sociolinguistic background, dialect, and vocal tract size and shape can contribute to across-speaker variabilities.Figure shows the major components of a typical speech recognition system. The digitized speech signal is first transformed into a set of useful measurements or features at a fixed rate,typically once every 10--20 msec (see sectionsand 11.3 for signal representation and digital signal processing, respectively). These measurements are then used to search for the most likely word candidate, making use of constraints imposed by the acoustic, lexical, and language models. Throughout this process, training data are used to determine the values of the model parameters.Figure: Components of a typical speech recognition system.Speech recognition systems attempt to model the sources ofvariability described above in several ways. At the level of signal representation, researchers have developed representations that emphasize perceptually important speaker-independent features of the signal, and de-emphasize speaker-dependent characteristics. At the acoustic phonetic level, speaker variability is typically modeled using statistical techniques applied to large amounts of data. Speaker adaptation algorithms have also been developed that adapt speaker-independent acoustic models to those of the current speaker during system use, (see section). Effects of linguistic context at the acoustic phonetic level are typically handled by training separate models forphonemes in different contexts; this is called context dependentacoustic modeling.Word level variability can be handled by allowing alternate pronunciations of words in representations known as pronunciation networks. Common alternate pronunciations of words, as well as effectsof dialect and accent are handled by allowing search algorithms to find alternate paths of phonemes through these networks. Statistical language models, based on estimates of the frequency of occurrence of word sequences, are often used to guide the search through the most probable sequence of words.The dominant recognition paradigm in the past fifteen years is known as hidden Markovmodels (HMM). An HMM is a doubly stochastic model, in which the generation of the underlying phoneme string and the frame-by-frame, surface acoustic realizations are both represented probabilistically as Markov processes, as discussed in sections,and 11.2. Neural networks have also been used to estimate the frame based scores; these scores are then integrated into HMM-based system architectures, in what has come to be known as hybrid systems, as described in section 11.5.An interesting feature of frame-based HMM systems is that speech segments are identified during the search process, rather thanexplicitly. An alternate approach is to first identify speech segments, then classify the segments and use the segment scores to recognize words.This approach has produced competitive recognition performance in several tasks.2 State of the ArtComments about the state-of-the-art need to be made in the contextof specific applications which reflect the constraints on the task. Moreover, different technologies are sometimes appropriate for different tasks. For example, when the vocabulary is small, the entire word can be modeled as a single unit. Such an approach is not practical for large vocabularies, where word models must be built up from subword units.Performance of speech recognition systems is typically described in terms of word error rate E, defined as:where N is the total number of words in the test set, and S, I, and D are the total number ofsubstitutions, insertions, and deletions, respectively.The past decade has witnessed significant progress in speech recognition technology. Word error rates continue to drop by a factor of 2 every two years. Substantial progress has been made in the basic technology, leading to the lowering of barriers to speaker independence, continuous speech, and large vocabularies. There are several factorsthat have contributed to this rapid progress. First, there is the coming of age of the HMM. HMM is powerful in that, with the availability of training data, the parameters of the model can be trained automatically to give optimal performance.Second, much effort has gone into the development of large speech corpora for systemdevelopment, training, and testing. Some of these corpora are designed for acoustic phonetic research, while others are highly task specific. Nowadays, it is not uncommon to have tens of thousands of sentences available for system training and testing. These corpora permit researchers to quantify the acoustic cues important for phonetic contrasts and to determine parameters of the recognizers in astatistically meaningful way. While many of these corpora (e.g., TIMIT, RM, ATIS, and WSJ; see section 12.3) were originally collected under the sponsorship of the U.S. Defense Advanced Research Projects Agency (ARPA) to spur human language technology development among its contractors, they have nevertheless gained world-wide acceptance (e.g., in Canada, France, Germany, Japan, and the U.K.) as standards on which to evaluate speech recognition.Third, progress has been brought about by the establishment of standards for performance evaluation. Only a decade ago, researchers trained and tested their systems using locally collected data, and had not been very careful in delineating training and testing sets. As a result, it was very difficult to compare performance across systems, and a system's performance typically degraded when it was presented with previously unseen data. The recent availability of a large body of data in the public domain, coupled with the specification of evaluation standards, has resulted in uniform documentation of test results, thuscontributing to greater reliability in monitoring progress (corpus development activities and evaluation methodologies are summarized in chapters 12 and 13 respectively).Finally, advances in computer technology have also indirectly influenced our progress. The availability of fast computers with inexpensive mass storage capabilities has enabled researchers to run many large scale experiments in a short amount of time. This means that the elapsed time between an idea and its implementation and evaluationis greatly reduced. In fact, speech recognition systems with reasonable performance can now run in real time using high-end workstations without additional hardware---a feat unimaginable only a few years ago.One of the most popular, and potentially most useful tasks with low perplexity (PP=11) is the recognition of digits. For American English, speaker-independent recognition of digit strings spoken continuously and restricted to telephone bandwidth can achieve an error rate of 0.3% when the string length is known.One of the best known moderate-perplexity tasks is the 1,000-wordso-called Resource Management (RM) task, in which inquiries can be made concerning various naval vessels in thePacific ocean. The best speaker-independent performance on the RM task is less than 4%, using a word-pair language model that constrains the possible words following a given word (PP=60). More recently, researchers have begun to address the issue of recognizing spontaneously generated speech. For example, in the Air Travel Information Service(ATIS) domain, word error rates of less than 3% has been reported for a vocabulary of nearly 2,000 words and a bigram language model with a perplexity of around 15.High perplexity tasks with a vocabulary of thousands of words are intended primarily for the dictation application. After working on isolated-word, speaker-dependent systems for many years, the community has since 1992 moved towards very-large-vocabulary (20,000 words and more), high-perplexity (PP?200), speaker-independent, continuous speech recognition. The best system in 1994 achieved an error rate of 7.2% on read sentences drawn from North America business news.With the steady improvements in speech recognition performance, systems are now being deployed within telephone and cellular networks in many countries. Within the next few years, speech recognition will be pervasive in telephone networks around the world. There are tremendous forces driving the development of the technology; in many countries, touch tone penetration is low, and voice is the only option for controlling automated services. In voice dialing, for example, users can dial 10--20 telephone numbers by voice (e.g., call home) after having enrolled their voices by saying the words associated with telephone numbers. AT&T, on the other hand, has installed a call routing system using speaker-independent word-spotting technology that can detect a few key phrases (e.g., person to person, calling card) in sentences such as: I want to charge it to my calling card.At present, several very large vocabulary dictation systems are available for document generation. These systems generally require speakers to pause between words. Their performance can be further enhanced if one can apply constraints of the specific domain such as dictating medical reports.Even though much progress is being made, machines are a long way from recognizing conversational speech. Word recognition rates on telephone conversations in the Switchboard corpus are around 50%. It will be many years before unlimited vocabulary, speaker-independent continuous dictation capability is realized.3 Future DirectionsIn 1992, the U.S. National Science Foundation sponsored a workshop to identify the key research challenges in the area of human language technology, and the infrastructure needed to support the work. The key research challenges are summarized in. Research in the following areas for speech recognition were identified:Robustness:In a robust system, performance degrades gracefully (rather than catastrophically) as conditions become more different from those under which it was trained. Differences in channel characteristics and acoustic environment should receive particular attention.Portability:Portability refers to the goal of rapidly designing, developing and deploying systems for new applications. At present, systems tend tosuffer significant degradation when moved to a new task. In order to return to peak performance, they must be trained on examples specific to the new task, which is time consuming and expensive.Adaptation:How can systems continuously adapt to changing conditions (new speakers, microphone, task, etc) and improve through use? Such adaptation can occur at many levels in systems, subword models, word pronunciations, language models, etc.Language Modeling:Current systems use statistical language models to help reduce the search space and resolve acoustic ambiguity. As vocabulary size grows and other constraints are relaxed to create more habitable systems, it will be increasingly important to get as much constraint as possible from language models; perhaps incorporating syntactic and semantic constraints that cannot be captured by purely statistical models.Confidence Measures:Most speech recognition systems assign scores to hypotheses for the purpose of rank ordering them. These scores do not provide a good indication of whether a hypothesis is correct or not, just that it is better than the other hypotheses. As we move to tasks that require actions, we need better methods to evaluate the absolute correctness of hypotheses.Out-of-Vocabulary Words:Systems are designed for use with a particular set of words, but system users may not know exactly which words are in the system vocabulary. This leads to a certain percentage of out-of-vocabulary words in natural conditions. Systems must have some method of detecting such out-of-vocabulary words, or they will end up mapping a word from the vocabulary onto the unknown word, causing an error.Spontaneous Speech:Systems that are deployed for real use must deal with a variety of spontaneous speech phenomena, such as filled pauses, false starts, hesitations, ungrammatical constructions and other common behaviors not found in read speech. Development on the ATIS task has resulted in progress in this area, but much work remains to be done.Prosody:Prosody refers to acoustic structure that extends over several segments or words. Stress, intonation, and rhythm convey important information for word recognition and the user's intentions (e.g., sarcasm, anger). Current systems do not capture prosodic structure. How to integrate prosodic information into the recognition architecture is a critical question that has not yet been answered.Modeling Dynamics:Systems assume a sequence of input frames which are treated as if they were independent. But it is known that perceptual cues for words and phonemes require the integration of features that reflect the movements of the articulators, which are dynamic in nature. How to modeldynamics and incorporate this information into recognition systems is an unsolved problem.语音识别舒维都，罗恩科尔，韦恩沃德麻省理工学院计算机科学实验室，剑桥，马萨诸塞州，美国俄勒冈科学与技术学院，波特兰，俄勒冈州，美国卡耐基梅隆大学，匹兹堡，宾夕法尼亚州，美国一定义问题语音识别是指音频信号的转换过程，被电话或麦克风的所捕获的一系列的消息。

如何在英语演讲中运用声音技巧

如何在英语演讲中运用声音技巧How to Use Vocal Techniques in English Speaking在英语演讲中运用声音技巧Effective communication is essential for success in any field. Whether you are a student, a professional, or aspiring to be a public speaker, mastering the art of English speaking can greatly enhance your abilities. While vocabulary and grammar are crucial components, paying attention to the way you use your voice can make a significant impact on how your message is received. In this article, we will explore the various vocal techniques that can be utilized to improve your English speaking skills.有效的沟通对于任何领域的成功至关重要。

无论你是学生、职业人士，还是渴望成为一名公众演讲者的人，掌握英语口语艺术可以极大地提升你的能力。

然而词汇和语法虽然至关重要，但是注意你如何运用声音在很大程度上决定了你的信息是如何被接受的。

在本文中，我们将探讨各种声音技巧，以提高你的英语口语能力。

1. Tone and Pitch语调和音高The tone and pitch of your voice play a crucial role in delivering your message effectively. Tone refers to the quality or character of your voice, while pitch refers to the highness or lowness of your vocal sound. By varying your tone and pitch, you can convey different emotions and emphasize key points in your speech.你的声音的语调和音高对于有效传递信息起着至关重要的作用。

《即兴口语表达》课程教学大纲

《即兴⼝语表达》课程教学⼤纲《即兴⼝语表达》课程教学⼤纲⼀、课程基本信息课程代码：课程名称：即兴⼝语表达英⽂名称：Impromptu Speech Communication课程类别：必修课学时：112学时#学分：7学分适⽤对象: 播⾳与主持艺术本科专业考核⽅式：考试先修课程：《普通话语⾳与播⾳发声I》《普通话语⾳与播⾳发声II》《播⾳创作基础》⼆、课程简介《即兴⼝语表达》课程是播⾳与主持艺术专业的⼀门专业基础课，也是播⾳与主持艺术专业的核⼼课程。

本课程⽴⾜我国播⾳与主持艺术⼯作的实际，同时借鉴新闻传播学的相关规律以及语⾔学和艺术学的相关知识，以有声语⾔表达为核⼼，以即兴⼝语表达训练为主要⽅式，主要介绍即兴⼝语表达的基本规律和基本技能，包括⼝头复述、⼝头描述、⼝头评述以及即兴解说、即兴语智和即兴幽默等内容。

本课程能够为学⽣提供较为全⾯、系统的即兴⼝语表达的基本技能训练，是对有声语⾔表达技巧的进⼀步巩固和提升，为⼴播电视节⽬主持打下基础。

Impromptu Speech Communication is not only a professional introductory course of Broadcasting and conduct, but also a Core curriculum of Broadcasting and conduct. According the fact of Broadcasting and conduct , the course borrow the laws of Information communications and relerant knowledge of Linguistics and art, in spoken word expressing as the core, to impromptu spoken language as the method, generally produce the basic rule and basic skills of impromptu spoken language, including oral retelling ,oral describing ,oral comments and impromptu explaining , impromptu language wisdom, impromptu humor. The course provide general systemic basic skills for impromptu oral expressing, improving the skills of spoken language expressing, for Radio and television emceeing lay a foundation.，三、课程性质与教学⽬的《即兴⼝语表达》课程是播⾳与主持艺术专业的⼀门专业基础课、必修课，也是播⾳与主持艺术专业的核⼼课程。

听说是英语学习成功的关键英语作文

听说是英语学习成功的关键英语作文全文共3篇示例，供读者参考篇1Listening is said to be the key to success in learning English. Many experts and language learners agree that listening plays a crucial role in mastering a new language. In this essay, I will explore why listening is important in English language learning and how it can contribute to success in mastering the language.To begin with, listening is essential for improving one's speaking and pronunciation skills. When we listen to native speakers, we are exposed to different accents, intonations, and expressions that help us develop our own speaking skills. By hearing how words and phrases are pronounced, we can imitate and practice them, which in turn improves our pronunciation. Additionally, listening helps us understand the natural flow and rhythm of the language, which is important for speaking fluently and confidently.Furthermore, listening is crucial for understanding English in various contexts. Through listening, we can grasp the meaning of words, phrases, and sentences in different situations, such ascasual conversations, formal speeches, or academic lectures. This exposure to a wide range of vocabulary and expressions helps us become more proficient in understanding and using English in real-life situations. Listening also helps us improve our comprehension skills, as we learn to follow and understand spoken instructions, conversations, and narratives.In addition, listening is a key component of language acquisition and retention. Research has shown that listening to a language repeatedly helps reinforce vocabulary and grammar structures in our memory. By listening to English regularly, we can internalize the language and make it a natural part of our linguistic repertoire. Listening also helps us develop our listening skills, such as identifying key information, understanding main ideas, and inferring meaning from context, all of which are important for effective communication.Moreover, listening is a valuable tool for cultural understanding and appreciation. Through listening to English speakers from different backgrounds and regions, we can learn about their customs, traditions, values, and beliefs. This exposure to diverse cultural perspectives enhances our intercultural competence and enables us to communicate more effectively with people from different cultural backgrounds. Listening alsohelps us appreciate the rich diversity of the English language and its various dialects, accents, and idiomatic expressions.In conclusion, listening is indeed the key to success in learning English. By listening attentively and actively to English speakers, we can improve our speaking and pronunciation skills, understand English in different contexts, acquire and retain vocabulary and grammar structures, develop our listening skills, and enhance our cultural understanding and appreciation. Therefore, I encourage all English language learners to make listening a priority in their language learning journey, as it is a powerful and effective way to achieve success in mastering the language.篇2Listening and Speaking: The Key to Success in English LearningEnglish is considered one of the most widely spoken languages in the world, and for many people, it is also one of the most challenging languages to learn. However, mastering English can open up a world of opportunities, from improved career prospects to enhanced travel experiences. So, what is the key to success in English learning? Many experts agree thatlistening and speaking skills play a crucial role in language acquisition.Listening is often considered a foundational skill in language learning. When we listen to English being spoken, we are exposed to the natural rhythms and intonations of the language. This exposure helps us develop our ear for English and improves our understanding of spoken language. Additionally, listening allows us to pick up on commonly used phrases, idioms, and expressions that may not be as easily understood through reading alone.To improve listening skills, it is essential to practice regularly. One effective way to do this is by listening to English podcasts, radio programs, and audiobooks. These resources providereal-life examples of English being spoken in various contexts, which can help learners become more familiar with the language. Additionally, watching English-language movies and TV shows with subtitles can also be beneficial, as it allows learners to hear spoken English while reading along.Speaking is another critical component of English language acquisition. By speaking English regularly, learners can improve their pronunciation, intonation, and fluency. Speaking also helpslearners develop their vocabulary and grammar skills, as they are required to actively use the language in real-time conversations.One effective way to practice speaking is by engaging in conversations with native English speakers or language exchange partners. This can be done through language exchange websites, language learning apps, or local conversation groups. By talking with others in English, learners can gain valuable feedback on their language skills and receive guidance on areas for improvement.In addition to speaking with others, learners can also practice speaking on their own. This can be done by recording and listening to oneself speaking in English, or by engaging in language drills and exercises that focus on pronunciation, intonation, and fluency. By practicing speaking regularly, learners can build confidence in their ability to communicate effectively in English.In conclusion, listening and speaking are integral components of English language learning, and mastering these skills can greatly contribute to success in language acquisition. By actively listening to English being spoken and practicing speaking with others, learners can improve their language skills and become more proficient in English. So, if you want tosucceed in learning English, focus on developing your listening and speaking abilities – they will take you a long way in your language learning journey.篇3Success in learning English is a goal for many people around the world. Whether you are a student, a professional, or an English language enthusiast, mastering this global language can open up countless opportunities and enrich your life in many ways. But what is the key to success in learning English? Many people believe that it all comes down to effective listening and speaking skills.Listening is often considered to be the first step in language learning, as it is through listening that we acquire new vocabulary, understand grammar rules, and develop our pronunciation. By listening to native speakers and various forms of English media such as movies, TV shows, radio programs, and podcasts, we can expose ourselves to different accents, intonations, and styles of speaking, which can greatly improve our listening comprehension skills. Additionally, listening to English speakers in real-life situations, such as during conversations with friends, colleagues, or strangers, can help uslearn how to understand and interpret spoken English more effectively.On the other hand, speaking is just as important as listening when it comes to mastering English. By practicing speaking on a regular basis, we can improve our fluency, accuracy, and confidence in using the language. Speaking with native speakers or other English learners can help us develop our pronunciation, intonation, and conversational skills. By engaging in discussions, debates, presentations, and other speaking activities, we can learn how to express our ideas, opinions, and emotions in English more confidently and effectively.In addition to listening and speaking, there are also other key factors that contribute to success in learning English. These include reading, writing, vocabulary acquisition, grammar mastery, cultural awareness, and motivation. By reading English books, articles, newspapers, and websites, we can improve our reading comprehension skills, expand our vocabulary, and enhance our knowledge of grammar and syntax. By writing essays, emails, reports, and other types of texts in English, we can practice our writing skills, develop our ability to organize and express our thoughts, and receive feedback on our language accuracy and coherence.Moreover, learning English is not just about mastering the language itself, but also about understanding the culture, traditions, values, and norms of English-speaking countries. By learning about British, American, Australian, Canadian, and other English-speaking cultures, we can gain a deeper appreciation and understanding of the language and its speakers. This can help us become more culturally sensitive, communicatively competent, and socially aware when interacting with English speakers in various contexts.Another important factor that influences success in learning English is motivation. Motivation can come from various sources, such as personal interests, career goals, academic aspirations, social needs, travel desires, and self-improvement ambitions. By setting clear, realistic, and measurable goals for our English learning, we can stay motivated, focused, and committed to our language learning journey. By seeking out opportunities to use English in real-life situations, such as studying abroad, attending language courses, participating in language exchange programs, watching English movies, and reading English books, we can stay engaged with the language and motivated to improve our proficiency.In conclusion, success in learning English relies on a combination of various skills, strategies, and attitudes, with listening and speaking playing a crucial role in language acquisition and communication. By developing our listening and speaking skills, along with reading, writing, vocabulary, grammar, cultural awareness, and motivation, we can enhance our English proficiency, expand our linguistic competence, and enrich our intercultural communication skills. Ultimately, the key to success in learning English lies in our ability to listen, speak, and engage with the language in meaningful and purposeful ways. So, keep listening, keep speaking, and keep learning! Success in English is within reach.。

胡壮麟语言学教程Chapter 2

15 Chapter 2
Chapter 2 Speech Sounds

Three main factors that decide a sound of consonant: 1) the participation of vocal cords (voiced or voiceless) 2) the place of articulation (bilabial, alveolar, dental, etc.) 3) the manner of articulation (stop, fricative, approximant, nasal, etc.)
9 Chapter 2
Chapter 2 Speech Sounds
Hale Waihona Puke 2.2 Speech organs voiceless sound: when the vocal folds are apart and the air can pass through easily. voiced sound: when the vocal folds are close together and the air stream causes them to vibrate.
（音位学） Phonological processes Phonology Distinctive features
3 Chapter 2
Chapter 2 Speech Sounds

2.1 Speech production and perception
A speech sound goes through a three-step process as shown below. Speaker A Speaker B speech speech speech production ---transmission ---perception Articulatory (acoustic) (auditory) the study of | the study of the physical | concerned with production of | properties of the sounds | the perception speech sounds | produced in speech | of speech sounds

英语演讲可俩俺的人

Reading the room
Eye contact
04
Dealing with tense emotions
Deep breathing is a relaxation technique that can help reduce anxiety and tension before a speech. It involves taking slow, deep breaths to calm down and relax the body and mind.
Determine the purpose of your speech: to inform, persuade, or entertain.
Determine the theme
Research and understand the background and context of your topic to provide a broader perspective.
By being well-prepared, you can feel more confident and relaxed when delivering your speech, which helps to control any tense emotions that may arise.
Vocabulary selection
03
Non verbal expression skills
body language
Posture
Stand tall and straight, with your shoulders back. This will help you appear confident and authoritative.

好的英语演讲稿应该具备哪些特点？

好的英语演讲稿应该具备哪些特点？Good English Speech CharacteristicsA good English speech is a powerful tool in communication, especially in this age of globalisation where people from diverse cultures and backgrounds interact. It is an excellent way of conveying ideas, opinions, and emotions in a clear, concise, and effective manner. A well-delivered speech can capture the attention of the audience, inspire them to take action, and leave a lasting impression. In this article, wewill explore the essential characteristics that make a good English speech.1. Clarity of MessageA good English speech should have a clear and concise message. The speaker should be able to articulate his/herideas in a way that the audience can easily understand. The message should be relevant and engaging to the audience and should be delivered in a manner that is easy to absorb.2. Conviction and ConfidenceA speaker must have a firm belief in the message they are delivering. The conviction will allow the speaker to deliver the speech with confidence and passion. A confident speakeris more likely to persuade the audience, and their audienceis more likely to remember and act on their message. Hence, the speaker should show confidence in his/her speech, both in demeanor and message delivery.3. Emotional IntelligenceEmotional intelligence in a speech refers to the ability of the speaker to connect with the audience emotionally. This includes the use of humor, storytelling, analogies, and rhetorical questions. Emotional intelligence ensures the audience is engaged and more likely to remember the message.4. Organization and StructureA well-organized speech is easy to understand, and the audience will follow the message flow without getting confused. An excellent speaker will structure their speech logically and ensure a smooth transition from one point to the next, keeping the attention of the audience throughout the speech.5. AppropriatenessThe speaker should tailor their speech to the audience, occasion, and culture or location. The appropriateness of the speech will make it more relatable, and the audience is more likely to see the relevance of the message and take action.6. Vocal VarietyAn excellent speaker should have good vocal variety in pitch, tone, emphasis, and speed, among others. A monotone voice can bore the audience and make it hard for them to concentrate on the message. Vocal variety is an essential part of communication and adds value to the speech.7. Body LanguageNon-verbal cues such as facial expressions, eye contact, and hand gestures play a massive role in communication. A good speaker uses body language to emphasize their message, connect with the audience and showcase confidence.8. Memorable ConclusionA good speech ends with a memorable conclusion that summarizes the speech and leaves a lasting impression on the audience. The conclusion should inspire and motivate the audience to take action, either on a personal or societal level.In conclusion, the above characteristics make a good English speech. A speaker should strive to ensure that their speech is relevant, engaging, and easily understood by the audience. With practice and preparation, anyone can deliver a compelling speech that inspires action and change.。

演讲稿英文配音

演讲稿英文配音Ladies and gentlemen, good morning!Today, I am honored to stand before you to deliver a speech on the topic of "The Power of English Voiceover". As we all know, English voiceover has become an essential part of various industries, including film, television, advertising, and education. It plays a crucial role in conveying messages, emotions, and information to the audience. In this speech, I will discuss the importance and impact of English voiceover, as well as the skills and techniques required for a successful performance.First and foremost, the power of English voiceover lies in its ability to bridge communication gaps. With the increasing globalization, English has become the most widely spoken language in the world. Through voiceover, content can be translated and delivered to a global audience, breaking down language barriers and connecting people from different cultures and backgrounds. This not only facilitates international communication but also promotes cultural exchange and understanding.Moreover, English voiceover has a significant impact on the entertainment industry. In films and television shows, voiceover brings characters to life and enhances the viewing experience. It conveys emotions, thoughts, and personality traits, allowing the audience to connect with the story on a deeper level. Additionally, in the advertising industry, a compelling voiceover can capture the attention of consumers, create brand recognition, and influence purchasing decisions. The power of English voiceover in entertainment and advertising cannot be underestimated.In addition to its impact, successful English voiceover requires a set of skills and techniques. A good voiceover artist must have a clear and articulate voice, excellent pronunciation, and the ability to convey emotions effectively. They should also possess the skill of timing and pacing, knowing when to pause, emphasize, or modulate their voice to match the visuals and convey the intended message. Furthermore, understanding the context and purpose of the content is essential for a successful voiceover performance.Whether it's a dramatic narration, a persuasive commercial, or an educational video, the voiceover artist must adapt their tone and style accordingly.In conclusion, the power of English voiceover is undeniable. It serves as a bridge for global communication, enhances entertainment experiences, and influences consumer behavior. As the demand for English voiceover continues to grow, it is essential for voiceover artists to hone their skills and adapt to the evolving industry. With the right techniques and a deep understanding of the impact of their work, voiceover artists can truly make a difference in the world of communication and entertainment.Thank you for your attention. Let's continue to appreciate and harness the power of English voiceover.。

如何说好普通话,英语作文

如何说好普通话,英语作文Mastering the Mandarin Chinese language can be a challenging but rewarding endeavor. As the most widely spoken language in the world with over 1.3 billion native speakers, Mandarin Chinese offers numerous benefits to those who are able to communicate effectively in it. Whether you are a student looking to expand your linguistic repertoire, a business professional seeking to navigate the Chinese market, or simply an individual interested in Chinese culture, learning to speak Mandarin well can open up a world of opportunities. In this essay, we will explore the key strategies and techniques that can help you become a proficient Mandarin speaker.The first and perhaps most crucial step in learning Mandarin is to develop a strong foundation in the language's phonetics. Mandarin Chinese is a tonal language, meaning that the same syllable can have different meanings depending on the tone with which it is pronounced. There are four main tones in Mandarin: the high level tone, the rising tone, the falling-rising tone, and the falling tone. Mastering these tones is essential for accurate pronunciation and clear communication. Beginners should start by practicing the tonesin isolation, focusing on the subtle differences in pitch and contour. As you become more comfortable, you can incorporate the tones into words and eventually entire sentences.Another fundamental aspect of Mandarin that requires diligent practice is the language's writing system. Mandarin Chinese utilizes thousands of unique characters, each representing a specific word or concept. While the sheer number of characters can be daunting, there are effective strategies to help you navigate this complex writing system. One approach is to focus on learning the most common and frequently used characters first, as they will serve as the building blocks for more advanced vocabulary. Additionally, understanding the basic components and structures of Chinese characters, known as radicals, can aid in the memorization process.Alongside phonetics and writing, vocabulary acquisition is a crucial component of becoming a proficient Mandarin speaker. Mandarin Chinese has a vast lexicon, with many words and phrases that have no direct equivalent in English. To build your vocabulary, engage in regular practice through activities such as flashcard study, reading children's books or news articles, and conversing with native speakers. It is also helpful to group words into semantic categories, such as family members, daily activities, or food items, as this can aid in the retention and recall of new vocabulary.In addition to mastering the foundational elements of the language, developing strong listening and speaking skills is essential for effective communication in Mandarin. Immerse yourself in the language by watching Chinese television shows, listening to Mandarin podcasts, or engaging in conversation practice with a language partner. Pay close attention to the rhythm, intonation, and natural flow of the language, and strive to mimic these patterns in your own speech. Regularly practicing conversational exchanges, whether with a tutor, a language exchange partner, or in a language learning group, can help you gain confidence and improve your fluency.Furthermore, understanding the cultural context and nuances of the Mandarin language is crucial for effective communication. Mandarin Chinese is deeply rooted in a rich cultural heritage, and understanding the underlying cultural references, social etiquette, and body language can greatly enhance your ability to interact with native speakers. Familiarize yourself with common Chinese idioms, proverbs, and cultural traditions, and be mindful of the importance of respect, hierarchy, and indirect communication in the Chinese context.Finally, maintaining a positive attitude and a willingness to learn and make mistakes is crucial in the journey of mastering Mandarin Chinese. Learning a new language, especially one as complex asMandarin, can be a daunting and frustrating process at times. However, by embracing a growth mindset, celebrating small victories, and persevering through challenges, you can steadily progress towards your goal of becoming a proficient Mandarin speaker.In conclusion, learning to speak Mandarin Chinese well requires a multifaceted approach that encompasses the mastery of phonetics, writing, vocabulary, listening, and speaking skills, as well as a deep understanding of the cultural context. By following the strategies and techniques outlined in this essay, you can embark on a rewarding journey towards fluency in one of the world's most widely spoken languages. With dedication, persistence, and a genuine passion for the language, you can unlock a wealth of personal and professional opportunities that come with the ability to communicate effectively in Mandarin Chinese.。

演讲比赛肢体语言的建议英语作文

演讲比赛肢体语言的建议英语作文Speech Contest Body Language TipsHi everyone! My name is Sam and I'm a 5th grader here to share some advice about using good body language for speech contests. Body language is how you hold and move your body when speaking, and it's really important! The judges don't just listen to the words you say, but also watch how you say them with your posture, hand gestures, facial expressions and more.When I first started doing speech contests a few years ago, my body language wasn't so hot. I would hunch over the podium, stare down at my note cards the whole time, and barely move at all except to turn my pages. My coaches gave me some great tips though, and now my body language skills are on point! Let me share what I've learned.Eye ContactOne of the biggest things is making eye contact with the audience and judges. This can be scary at first, but it makes your speech so much more engaging and powerful. Here's how to do it right:• Look up from your notes and make eye contact with different people in the audience frequently as you speak• Don't just stare at one person the whole time, that's creepy! Scan the room and make eye contact with new people every few seconds• When making eye contact, don't just glance at someone sideways. Face them directly and hold eye contact for a moment before moving onI like to pick out a few friendly faces in different sections of the audience to lock eyes with at key moments. It makes me feel more connected to my listeners.GesturesUsing hand gestures and body movements is another way to keep people's attention and drive home your points. There are all kinds of gestures you can use, like:• Sweeping arm movements to emphasize important lines• Counting off points on your fingers as you make them• Cutting one han d through the air to show contrast or rejection• Clenching your fist or giving a thumbs up for feeling enthusiastic• Holding your palms up in a questioning gestureThe trick is to make gestures that feel natural, not like crazy robotic arm-waving. Your gestures should match and emphasize what you're saying, not distract from it. Practice makes perfect!Facial ExpressionsDon't forget to use lots of animated facial expressions too. This makes you seem more passionate and brings your words to life. Smile, raise your eyebrows, look surprised, furrow your brow - whatever fits the mood and message of that moment.One expression coaches always remind me of is "the questioning look" - raising your eyebrows and looking inquisitive when asking a rhetorical question. This pulls the audience in and makes them think about the answer.Posture and MovementPosture and movement around the stage area are key too. Here are some posture tips:• Stand up straight with your shoulders back - don't slouch or hunch over• Plant your feet about shoulder-width apart for a stable, grounded stance• Lean slightly forward at the waist to project your voice better• Don't grip the podium tightly the whole time - hold it loosely or move away from itAnd for movement:• Don't just plant yourself behind the podium - move around the stage area• Take a few steps left or right to reenergize your presence• Step forward when making a passionate point, then pause for emphasis• Be sure to face the audience as you move - don't walk in circlesUsing good posture and limited but purposeful movement around the stage area conveys confidence and keeps you from seeming stiff or static.Recover SmoothlyEven with lots of practice, we all make little mistakes sometimes - stumbling over a word, dropping our page of notes, or getting flustered for a moment. When that happens, don'tpanic! The judges understand. Just take a pause, smile confidently, then keep going like a pro.If you drop your notes, bend down and calmly pick them up. As you're bending over, take a nice deep breath, then keep on rolling when you stand back up. If you get thrown off and need to restart a sentence, just laugh it off, re-gather yourself for a second, and re-launch into that line. The judges will be impressed by how poised and unruffled you can be.So those are my top tips for powerful body language in a speech contest! Using strong eye contact, gestures, facial expressions, good posture and some movement will make you seem so confident and engaging. But always be sure to practice, practice, practice beforehand until it feels natural. You've got this!Let me leave you with one last big reminder: Even though your words are super important, body language can make or break a speech. The judges and audience will be watching and evaluating your body language just as closely as what you actually say. So use your body language skills to reinforce and elevate your message!Okay, that's all from me. Just get out there, speak with confidence and let your body do the talking. Work on all thesebody language elements and you're sure to be a showstopper! Thanks for listening, and best of luck in your next speech contest!。

克服发音问题的更好方法英语作文

克服发音问题的更好方法英语作文全文共10篇示例，供读者参考篇1Title: The Best Way to Improve PronunciationHey guys! Today, I want to talk about a really important topic – how to improve your pronunciation in English. I know it can be tricky sometimes, but don't worry, I've got some tips to help you out.First of all, the best way to improve your pronunciation is to practice, practice, practice! You can start by listening to English songs, watching English movies or TV shows, and even talking to native speakers. The more you hear and speak English, the better you will get at pronouncing words correctly.Another great way to improve your pronunciation is to pay attention to the sounds of the words. English has a lot of tricky sounds, like the "th" sound in words like "this" or "that." Practice saying these words out loud and try to mimic the sounds as closely as you can.You can also try recording yourself speaking and then listen back to see how you sound. This can help you identify which words or sounds you need to work on. Don't be afraid to ask for feedback from your teachers or friends too – they can give you helpful tips on how to improve.Lastly, don't get discouraged if you make mistakes. Pronunciation takes time and practice, so just keep at it and you will see improvement over time. Remember, the more you practice, the better you will get!So there you have it – some tips on how to improve your pronunciation in English. Just keep practicing and don't give up. You've got this!篇2Hi everyone, today I want to talk about how to overcome pronunciation problems in English. Sometimes it can be hard to say some words correctly, but there are some tricks you can use to improve your pronunciation.First, try to practice speaking English every day. The more you practice, the better you will get. You can read books out loud, watch English movies or TV shows, or even talk to yourself in the mirror. It might feel silly at first, but it really helps!Another tip is to listen carefully to how native speakers pronounce words. You can listen to English songs, podcasts, or even videos on YouTube. Pay attention to how they pronounce certain sounds and try to imitate them.It’s also important to focus on the sounds that are difficult for you. For example, if you have trouble with the “th” sound, practice saying words like “this” or “that” o ver and over again until you get it right.Don’t be afraid to ask for help. If you have a teacher or a friend who is a native English speaker, ask them to help you practice. They can give you tips on how to improve your pronunciation and correct you when you make mistakes.Remember, it’s okay to make mistakes. Everyone struggles with pronunciation at some point. Just keep practicing and you will get better over time. Believe in yourself and don’t give up!I hope these tips will help you overcome your pronunciation problems and become more confident in speaking English. Keep practicing and you will improve little by little. Good luck!篇3Hey guys, today I wanna talk about how to overcome pronunciation problems in English. It can be tough sometimes, but with a few tricks and tips, you can improve your pronunciation in no time!First of all, practice makes perfect! Try to listen to English songs, watch English movies, and even chat with your friends in English. The more you hear the language, the better you'll become at pronouncing words correctly.Secondly, pay attention to phonics. English has some tricky sounds that are not present in other languages. Take some time to learn how to pronounce them correctly. You can use online resources or ask your teacher for help.Another tip is to record yourself speaking and then listen to it. This way, you can identify the areas where you need improvement and work on them.Lastly, don't be afraid to make mistakes. It's all part of the learning process. Practice, practice, practice, and soon enough, you'll be speaking English like a native!So, don't give up and keep practicing. Remember, the key to overcoming pronunciation problems in English is perseverance and determination. Good luck!篇4Hey guys, do you ever struggle with your English pronunciation? Well, don't worry! I'm here to share some awesome tips with you on how to overcome your pronunciation problems.First of all, it's super important to practice speaking English every day. The more you speak, the better you'll get at pronouncing words correctly. You can even practice with your friends or family members to make it more fun!Secondly, listen to native English speakers as much as you can. This will help you get a feel for how words should sound when spoken correctly. You can watch English movies, listen to English songs, or even follow English-speaking YouTubers to improve your pronunciation skills.Another great tip is to use a pronunciation dictionary or app. These tools can help you learn how to pronounce words by showing you the correct way to say them. You can also record yourself speaking and compare it to the pronunciation guide to see where you need to improve.Lastly, don't be afraid to ask for help. If you're unsure about how to pronounce a word, ask your teacher or a classmate for guidance. They will be more than happy to help you out.Remember, practice makes perfect! So keep practicing, keep listening, and keep asking for help. With dedication and hard work, you'll be speaking English like a pro in no time. Good luck!篇5Title: A Better Way to Overcome Pronunciation ProblemsHey guys! Today I want to talk about how to get better at pronouncing English words. I know it can be super hard sometimes, but don't worry, I've got some tips that will help you out!First of all, it's important to listen to native English speakers as much as possible. You can watch English movies, listen to English songs, or even just chat with people who speak English well. The more you hear how words are supposed to sound, the easier it will be for you to pronounce them correctly.Another thing you can do is practice, practice, practice! Try saying the words out loud over and over again until you get them right. You can also record yourself speaking and then listenback to see where you might be making mistakes. It might sound a bit silly, but trust me, it really works!If you're still having trouble, don't be afraid to ask for help. Your teacher or a friend who is good at English can give you some tips on how to improve your pronunciation. They might even be able to recommend some exercises or resources that can help you out.Remember, it's totally normal to struggle with pronunciation sometimes. Just keep practicing and don't get discouraged. You'll get better with time and effort. Good luck!篇6Title: The Best Ways to Improve PronunciationHey guys! Today I want to talk about how to get better at pronouncing English words. I know it can be really hard sometimes, but don't worry, I've got some great tips to help you out!First of all, practice makes perfect! The more you practice saying words out loud, the better you will get at pronouncing them. You can try reading English books, watching Englishmovies or even singing English songs. This way, you can hear how native speakers pronounce words and try to copy them.Secondly, pay attention to the sounds of the words. English has some sounds that are different from your own language, so it's important to listen carefully and practice those tricky sounds. For example, the "th" sound in words like "this" and "that" can be hard for some people, so make sure to practice it often.Another good way to improve pronunciation is to use online resources. There are tons of websites and apps that can help you practice your pronunciation, such as Duolingo or Pronunciation Club. These tools can give you feedback on your pronunciation and help you improve over time.Lastly, don't be afraid to ask for help! If you're struggling with a particular sound or word, ask a teacher or a friend to help you practice. They can give you tips and pointers on how to improve your pronunciation.Remember, the key to improving your pronunciation is practice, practice, practice! So keep practicing and don't give up. You'll be speaking English like a pro in no time!篇7Hello everyone, today I want to talk about how to overcome pronunciation problems in English. Sometimes it can be hard to say some words correctly, but with practice and some tips, we can improve our pronunciation!First of all, it’s important to listen carefully to how native speakers say the words. You can watch movies, listen to music, or even practice with a language partner. The more you listen, the better you will understand how to say the words correctly.Next, try to break down the word into smaller parts. This can help you focus on each sound and make it easier to pronounce. Practice saying each part slowly and then put them together to say the whole word.Another tip is to practice tongue twisters. They are fun and can help you improve your pronunciation skills. You can find many tongue twisters online and challenge yourself to say them faster and faster.Additionally, don’t be afraid to make mistakes. It’s okay to not pronounce a word perfectly at first. Keep practicing and you will get better over time. Remember, practice makes perfect!In conclusion, improving your pronunciation in English takes time and practice. By listening to native speakers, breaking downwords, practicing tongue twisters, and being patient with yourself, you can overcome pronunciation problems. Keep practicing and don’t give up! You can do it!篇8Oh no, I always have trouble with my pronunciation whenI'm speaking English! It's so hard to say some words correctly and people always have a hard time understanding me. But I don't want that to stop me from improving my English skills. So I've come up with some better ways to overcome my pronunciation problems.The first thing I like to do is practice with my friends. We like to play games where we have to say words correctly and whoever gets it wrong has to do a silly dance. It's so much fun and it helps me remember how to pronounce the words properly. Plus, my friends can give me tips on how to improve my pronunciation.Another thing I like to do is watch English movies and listen to English songs. I pay close attention to how the actors and singers pronounce the words and try to imitate them. Sometimes I even pause the movie or song and practice saying the wordsout loud. It's a great way to train my ears and tongue to get used to the sounds of English.I also use online resources like pronunciation websites and apps. They have exercises and lessons that help me practice my pronunciation in a fun and interactive way. I especially like the voice recognition feature where I can record myself saying words and get feedback on how well I pronounced them.Finally, I ask my English teacher for help. They can give me personalized tips and exercises to improve my pronunciation. Whenever I have trouble with a word, I write it down and ask my teacher to help me practice saying it correctly. They are always so supportive and encouraging, which motivates me to keep working on my pronunciation.By using these methods, I've noticed a big improvement in my pronunciation. People can understand me better when I speak English and I feel more confident in my language skills. So don't be afraid to work on your pronunciation, there are many fun and effective ways to overcome this challenge. Just keep practicing and you'll get better in no time!篇9Hey guys! Today I want to talk to you about how to overcome pronunciation problems in English. Sometimes it can be really hard to say certain words or sounds correctly, but with practice and some helpful tips, you can improve your pronunciation skills.First of all, it's important to listen carefully to native speakers and try to imitate the way they say words. You can watch English TV shows or movies, listen to English songs, or even practice speaking with English-speaking friends or teachers. The more you listen and practice, the better you will become at pronouncing words correctly.Secondly, you can use tongue twisters to help improve your pronunciation. Tongue twisters are phrases that are difficult to say quickly and correctly, but practicing them can help you improve your pronunciation skills. Here's an example of a tongue twister you can try: "She sells seashells by the seashore." Try saying it quickly and see if you can get all the sounds right!Another tip is to break down words into smaller parts and practice saying each part separately before putting them together. This can help you focus on the specific sounds in a word and make it easier to pronounce correctly.Lastly, don't be afraid to ask for help or feedback from others. Your teachers, friends, or language exchange partners can give you advice on how to improve your pronunciation and correct any mistakes you may be making.Remember, practice makes perfect, so keep practicing and don't give up! With time and effort, you can overcome your pronunciation problems and improve your English skills. Good luck!篇10Title: How to Improve Your PronunciationHey guys! Do you ever struggle with pronouncing some English words? Don’t worry, it happens to all of us! But there are some really cool ways to help you get better at it. Let me tell you about them!First of all, practice makes perfect, right? You can try reading English books out loud or watching English movies and repeating after the actors. This way, you can hear how the words should sound and practice saying them yourself.Secondly, you can ask your teacher or a friend to help you. They can listen to you speak and give you some tips on how toimprove your pronunciation. Don’t be afraid to ask for help, everyone needs a little push sometimes!Another fun way to practice is by singing along to English songs. Music is a great way to learn new words and improve your pronunciation. Plus, it’s s uper fun and you can do it anywhere, anytime.Last but not least, try using language learning apps or websites. They often have exercises and games to help you with your pronunciation. It’s like playing a game while learning, how cool is that?So d on’t worry if you struggle with pronunciation, just keep practicing and trying out these tips. You’ll get better in no time! Just remember, practice makes perfect! Good luck!。

专业的语音发音技巧

专业的语音发音技巧Language is a powerful tool for communication, and pronunciation plays a crucial role in ensuring that our intended messages are effectively conveyed. Mastering professional pronunciation skills can greatly enhance our ability to express ourselves clearly and confidently. In this article, we will explore some essential techniques and tips for improving our language pronunciation.1. Develop awareness of phoneticsPhonetics is the study of the sounds used in language. By developing an understanding of basic phonetics principles, we can identify and differentiate between different sounds. This awareness is essential for improving our pronunciation. For example, learning about vowel and consonant sounds, syllable stress, and intonation patterns can significantly enhance our ability to reproduce accurate pronunciation.2. Practice with native speakersOne of the best ways to improve pronunciation is to practice with native speakers. Engaging in conversations and interacting with those who have a strong command of the language allows us to observe and imitate their pronunciation. Native speakers can provide valuable feedback and correct any errors or mispronunciations we may have. This practice enables us to refine our pronunciation skills and develop a more natural-sounding speech.3. Utilize technology and resourcesIn the digital age, we are fortunate to have access to various technological tools and resources that can aid our pronunciation practice. Online platforms, mobile applications, and software programs offer interactive exercises, audio clips, and pronunciation guides to help us master specific sounds or words. These resources can be utilized as valuable aids in our daily practice to refine our pronunciation skills.4. Record and self-evaluateRecording ourselves while speaking or reading aloud allows us to listen to our pronunciation and identify areas for improvement. Paying attention to our articulation, stress patterns, and intonation can help us pinpoint any weaknesses. By self-evaluating, we become more aware of our pronunciation errors and can take steps to correct them. This process of self-reflection and improvement is crucial in honing our pronunciation skills.5. Mimic native speakersMimicking native speakers is an effective technique for developing accurate pronunciation. By closely observing and imitating their mouth movements, stress patterns, and intonation, we can acquire a better understanding of how specific sounds are produced. Moreover, mimicking native speakers helps us internalize the correct pronunciation naturally and effortlessly. This emulation process should be complemented by regular practice to ensure long-term improvement.6. Seek professional guidanceFor those who desire a more structured approach to improving their pronunciation, seeking professional guidance through language courses orhiring a language tutor can be highly beneficial. Experienced instructors can provide personalized feedback, address specific pronunciation challenges, and offer targeted exercises to target problem areas. Their expertise and guidance can significantly accelerate our progress in developing professional pronunciation skills.7. Practice regularlyLike any skill, consistent practice is key to mastering pronunciation. Regular practice sessions, even for short periods, can yield significant improvements over time. Incorporating pronunciation exercises into our daily routine, such as reading aloud, watching videos or movies in the target language, and engaging in conversations, will help solidify newly acquired pronunciation skills.ConclusionMastering professional pronunciation skills requires dedication, practice, and a commitment to continuous improvement. By developing awareness of phonetics, practicing with native speakers, utilizing technology and resources, self-evaluating, mimicking native speakers, seeking professional guidance, and engaging in regular practice, we can refine our pronunciation and become more effective communicators. Remember, pronunciation is a journey, and with determination and perseverance, success is within reach.。

英语演讲技巧双语

英语演讲技巧双语English Speech Techniques - BilingualEnglish speech techniques play a significant role in effective communication. Whether you are speaking in English as a first or second language, these techniques can help you deliver a powerful bilingual speech. Here are some tips to enhance your English speech skills:1. Use simple and clear language: Avoid complex vocabulary and jargon that may confuse your audience, especially if they have limited English proficiency. Opt for simple words and phrases that are easy to understand.1. 使用简单清晰的语言：避免使用复杂的词汇和行话，以避免让听众困惑，尤其是那些英语能力有限的人。

选择简单易懂的词语和短语。

2. Speak at a moderate pace: Avoid speaking too fast or too slow. Find a pace that allows your audience to follow along easily. Use pauses appropriately to emphasize important points and give your listeners time to digest the information.2. 以适度的速度说话：避免说得太快或太慢。

雅思口语之流利地开口说英语

雅思口语之流利地开口说英语雅思口语考试的四个评分标准: Fluency, Vocabulary, Grammar, Pronunciation.这四个标准不是分开的，而都是紧密相连、相互影响的。

因为在说英语时，你的词汇量、发音以及语法会直接影响你的流利程度。

所以，英语口语能力需要全面发展。

什么影响口语流利度?1 .Vocabulary 词汇量在口语考试的时候，使用合适正确的词汇是很关键的。

如果你的词汇量不够大;或者一味地使用你掌握得不好、但却很复杂的词汇;再或者你分不清近型词、近义词、或者一个词的不同形式，比如economic, economy和economics 等，这些都会直接影响你的流利度。

解决方法要解决这个问题，你需要让你掌握的词汇都变成主动词汇(active vocabulary)。

相信很多同学现在掌握的词汇都是被动词汇(passive vocabulary)，被动词汇就是你认识但是却不会使用的单词;而主动词汇才是那些你既认识又能正确运用的词。

所以，把自己的掌握的词汇全部由被动变主动，是提高口语流利度的非常关键的一步。

Key tip：在考试的时候，如果你不确定某个单词的用法是否正确，那么就不要用这个词，因为，如果在考试时你把词汇用错了，那么就会拉低口语考试中的词汇评分。

如何把被动词汇变成主动词汇在语境中重复使用单词或者短语，可以有效地帮助你把被动词汇变主动。

最好的方法就是把每个单词代入至少3-4个不同的语境(提问、回答或者对话)中练习使用。

只有通过这样不断地重复，你才能真正地学会使用单词。

2. Grammar 语法如果你对某个语法知识掌握的不熟练，在口语考试时，你就会停顿下来想语法规则，这就会大大影响你的流利度。

解决方法多练习使用语法，把正确的语法规则练成一种自然地反应，这样你在和考官对话的时候，就能脱口而出正确的句子。

以下对口译声音控制基本要求

口译声音控制基本要求1. 引言口译声音控制是指在进行口译工作时，通过采用合适的声音控制技巧和方法，使得口译表达更加清晰准确、有效传达信息。

良好的声音控制能够提高口译质量，增强听众的理解和接受程度。

本文将介绍口译声音控制的基本要求和技巧。

2. 声音基本要求2.1 音量•音量适中：不过分大声或小声，保持与听众距离相适应的音量。

•音量稳定：避免出现突然变大或变小的情况，保持稳定性。

2.2 语速•语速适中：不过快或过慢，以便听众能够理解和接受。

•注意节奏感：避免单调的语速，注意在适当位置停顿或加快语速以增加表达力。

2.3 发音•准确发音：正确发出每个单词，避免模糊不清或混淆。

•注意重读：准确把握每个单词中的重读部分，以增强语调的表达力。

2.4 音调•自然音调：保持自然而真实的音调，避免刻意夸张或死板。

•注意语气：根据句子的意思和语境，适当调整音调以传达情感和信息。

3. 声音控制技巧3.1 呼吸控制•深呼吸：在口译过程中保持深而稳定的呼吸，以保持声音稳定和持久。

•合理安排呼吸时间：在合适的位置进行短暂停顿，以确保正常呼吸并避免喘息。

3.2 身体姿势•笔直挺胸：保持良好的身体姿势，有利于声音的产生和传播。

•放松肩膀和颈部：避免紧张导致肩膀和颈部紧绷，影响声音质量。

3.3 节奏感掌握•把握句子节奏：通过合理把握句子中的重要词汇、短语和标点符号，掌握句子的节奏感。

•合理运用停顿：在适当位置进行停顿，以增加表达力和让听众有时间理解。

3.4 强调和语调•适当强调关键词：在句子中适当强调关键词，以突出重点和加强信息传达。

•合理运用语调：根据句子的意思和情感需要，合理运用升降调、上升降调等语音特点。

3.5 形象化表达•利用肢体语言：通过合理运用肢体语言来配合口译，增加表达力和吸引力。

•注意面部表情：保持自然而积极的面部表情，使得口译更具亲和力。

4. 实践与训练4.1 口语练习•多读多练：进行大量的口语练习，提高发音准确性和流利度。

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S PEECH B UILDER:Facilitating Spoken Dialogue System DevelopmentJames Glass and Eugene WeinsteinSpoken Language Systems GroupMIT Laboratory for Computer ScienceCambridge,Massachusetts02139,USAglass,ecoder@AbstractIn this paper we report our attempts to facilitate the creation of mixed-initiative spoken dialogue systems for both novice and experienced developers of human language technology.Our ef-forts have resulted in the creation of a utility called S PEECH-B UILDER,which allows developers to specify linguistic infor-mation about their domains,and rapidly create spoken dialogue interfaces to them.S PEECH B UILDER has been used to create domains providing access to structured information contained in a relational database,as well as to provide human language interfaces to control or transaction-based applications.1.IntroductionAs anyone who has tried to create a mixed-initiative spoken dia-logue system knows,building a system which interacts compe-tently with users,while allowing them freedom in what they can say and when they can say it during a conversation,is a signiﬁ-cant challenge.For this reason,many systems avoid this tactic, and instead take a more strategic approach which focuses on a directed dialogue.In fact,many researchers argue that conver-sational,mixed-initiative dialogue systems may not be worth pursuing,both for practical and philosophical reasons.Cer-tainly,there are many technical difﬁculties to overcome,which include recognizing and understanding conversational speech, generating reasonable and natural responses,and managing a ﬂexible dialogue strategy.Over the past decade,the Spoken Language Systems Group at the MIT Laboratory for Computer Science has been actively developing the human language technologies necessary for cre-ating such conversational human-machine interfaces.In recent years we have created several systems which have been publicly deployed on toll-free telephone numbers in North America,in-cluding systems providing access to information about weather forecasts,ﬂight status,andﬂight schedules and prices[1,2].Although these applications have been successful,there are limited resources at MIT to develop a large number of new do-mains.To address this issue,we have recently set out to make it easier to rapidly prototype new mixed-initiative conversational systems.Unlike other portability efforts that we are aware of, which tend to employ directed-dialogue strategies(e.g.,[3]), our goal is to enable the kinds of natural,mixed-initiative sys-tems which are now created manually by a relatively small group of expert developers.Names in alphabetical order.This research was supported by DARPA under contract N66001-99-1-8904monitored through Naval Command,Control and Ocean Surveillance Center and under an in-dustrial consortium supporting the MIT Oxygen Alliance.In this paper we describe our initial efforts in developing a utility called S PEECH B UILDER.The next section describes the architecture we have adopted.This is followed by a descrip-tion of the human language technologies which are deployed in S PEECH B UILDER,and the knowledge representation it uses. We then describe the current state of development,followed by ongoing and future activities in this project.2.System ArchitectureThe approach that we have adopted for developing the S PEECH-B UILDER utility has been to leverage the basic technology which is deployed in our more sophisticated conversational sys-tems.There are many reasons for doing this.First,in addi-tion to developing a programmable client-server architecture for conversational systems[4],we have devoted considerable effort over the last decade to improving human language technology (HLT)in speech recognition,language understanding,language generation,discourse and dialogue,and most recently,speech synthesis.By employing these HLT components we minimize duplication of effort,and maximize our ability to adopt any technical advances which are made in any of these areas.Sec-ond,by using our most advanced HLT components,we widen the pool of potential developers to include both novices and ex-perts,since the latter can use S PEECH B UILDER to rapidly pro-totype a new domain(a very useful feature)and subsequently modify it manually.Third,since we are not limiting any of the HLT capabilities in any way,we allow for the potential for S PEECH B UILDER-created systems to eventually scale up to the same level of sophistication as our most capable systems. Lastly,by focusing attention on portability as a major issue, we can potentially identify weaknesses in some of our existing HLT components.This can lead to better solutions,which will ultimately beneﬁt all of our conversational systems.We have made a conscious decision to have as simple an interface as possible for the user,while providing mechanisms to incorporate any needed complexities.For example,develop-ers do not specify natural language grammars for their domain. Instead,they specify the basic semantic concepts(called keys), and provide examples of user utterances which trigger different system behaviors(called actions).The system then uses these inputs to conﬁgure the language understanding grammar auto-matically.The developer can optionally create additional hier-archy in the grammar by using bracketing to label portions of the example sentences as being subject to a particular structure. Table1contains example sentences and their corresponding key/action representations encoded as CGI parameters(which are used for one kind of S PEECH B UILDER conﬁguration).Figure1:S PEECH B UILDER conﬁguration for database access. turn on the lights in the kitchenaction=set&frame=(object=lights,room=kitchen,value=on)will it be raining in Boston on Fridayaction=verify&frame=(city=Boston,day=Friday,property=rain) show me the Chinese restaurants on Main Streetaction=identify&frame=(object=(type=restaurant,cuisine=Chinese,on=(street=Main,ext=Street)))I want toﬂy from Boston to San Francisco arriving before ten a m action=list&frame=(src=BOS,dest=SFO,arrival time=(relative=before,time=(hour=10,xm=AM)))Table1:Example sentences and their CGI representations.2.1.S PEECH B UILDER ConﬁgurationsThere are currently two ways in which a S PEECH B UILDER ap-plication can be conﬁgured.Theﬁrst conﬁguration can be used by a developer to create a speech-based interface to structured data.There is no programming required.As shown in Figure1, this model makes use of the GALAXY architecture and all of the associated HLT components to access information stored in a relational database which is populated by the developer.This database table is used along with the semantic concepts and ex-ample utterances to automatically conﬁgure the speech recog-nition,language understanding,language generation(including both SQL and response generation),and discourse components (described in Section3).A generic dialogue manager handles user interactions with the database.Armed with a table of struc-tured data,an experienced developer can use S PEECH B UILDER to create a prototype system in a matter of minutes.The second possible S PEECH B UILDER conﬁguration pro-vides the developer with total control over the application func-tionality,as well as the discourse,dialogue,and response gener-ation capabilities[5].In this model,the developer creates a pro-gram implementing domain-speciﬁc functionality and deploys it on a CGI-enabled web server.As shown in Figure2,this con-ﬁguration uses a subset of the GALAXY components.The se-mantic frame is converted to the CGI parameter representation shown in Table1by means of the language generation compo-nent and is sent to the developer CGI application using HTTP.Since the developer CGI application is stateless,the S PEECH B UILDER server maintains a dialogue state variable which is exchanged with the CGI application on every turn.It is the responsibility of the application to decide what information, if any,to inherit from this state variable,and what information to retain for the next dialogue turn.Table2shows how the state information can beused to keep track of local discourse context.Figure2:Alternative S PEECH B UILDER conﬁguration. what is the phone number of John Smithaction=identify&frame=(property=phone,name=John+Smith) what about his email addressaction=identify&frame=(property=email)&history=(property=phone,name=John+Smith)what about Jane Doeaction=identify&frame=(name=Jane+Doe)&history=(property=email,name=John+Smith)Table2:Example interaction using the dialogue state variable.2.2.Web-Based InterfaceS PEECH B UILDER employs a web-based interface,and is im-plemented via a number of Perl CGI scripts.The web interface allows the developer to manipulate all of the domain speciﬁcs, such as keys and actions,query responses,and vocabulary word pronunciations.These domain speciﬁcs are stored in an XML document,and the developer has the ability to edit thisﬁle man-ually without using the online interface.The web interface con-tains a facility for downloading and uploading the XML repre-sentation of a domain,and for uploading CSV(comma sepa-rated value)representations of data tables.The developer also uses the web interface to compile the domain(that is,to con-ﬁgure the GALAXY servers according to domain speciﬁcs),and to start and stop the runtime modules of the domain.Once a domain has been compiled,the developer can also examine the parse tree and semantic frame produced for each example sen-tence.The interface makes it very easy for a developer to con-tinually modify,recompile,and redeploy a domain during the development cycle.3.Human Language TechnologiesAs shown in Figure1,S PEECH B UILDER makes use of all the major GALAXY components[4].The programmable hub exe-cutes a program which was created speciﬁcally for the S PEECH-B UILDER application,although it contains all of the function-ality of the programs used by our main systems.A speciﬁc version of the hub program is conﬁgured for each developer.The speech recognizer is conﬁgured to use generic telephone-based acoustic models,and is connected to the lan-guage understanding component via an-best interface[6]. Since users may speak words which are not speciﬁed in the vo-cabulary,we have incorporated an out-of-vocabulary model[7]. Baseform pronunciations which do not occur in our large on-line dictionaries are generated by rule[8].S PEECH B UILDER provides an editing facility for developers to modify pronunci-ations.The recognizer deploys a hierarchical-gram grammarKey Examplescolor red,green blueday Monday,Tuesday,Wednesdayroom living room,dining room,kitchenTable3:Examples of concept keys.derived from the language understanding grammar rules and the example sentences provided by the developer.For language understanding,S PEECH B UILDER conﬁgures a grammarﬁle as well as aﬁle for converting full or partial parses into a meaning representation[9].Language understand-ing is conﬁgured to back off to robust parsing(i.e.,concept spotting)when no full parse is available.The discourse server is based on a new component which performs inheritance and masking after an internal electronic‘E-form’has been gener-ated from the semantic frame[2].The dialogue management server was modeled after the functionality of our main systems[2].Since this component still tends to be extensively customized for every domain,we created a simple generic server which is intended to handle the range of situations which can arise in database query domains. We fully expect this component to increase in complexity as we consider a wider range of domains.The language generation component is actually used for generating three different outputs,as it is in our main sys-tems[10].Theﬁrst use of generation is for creating the‘E-form’representation used by the discourse and dialogue com-ponents.The second use is to generate an SQL query for use by the database server.The third use is to generate a response to the user which is vocalized using the speech synthesizer.4.Knowledge Representation Linguistic constraints are speciﬁed by a developer in terms of a set of concept keys and sentence-level actions via the web interface.Each is described in more detail in the following.4.1.Concept KeysConcept keys usually deﬁne classes of semantically equivalent words or word sequences.All the entries of a key class should play the same role in an utterance.Concept keys can be ex-tracted from the database table or can be manually speciﬁed by the developer through the web-based interface.In order to appear in the semantic frame a concept must be a member of a concept key class.A regularization mechanism allows the developer to specify variations on the spoken form(e.g.,“Philadelphia,”vs.“Philly”)that map to a standardized form(e.g.,“PHL”).Table3contains examples of concept keys.4.2.Sentence-level ActionsActions deﬁne classes of functionally equivalent sentences,so that all the entries of an action class perform the same operation in the application.Actions are example queries that one might use in talking to the domain.Action labels determine the clause name of the semantic frame produced by the language under-standing component.S PEECH B UILDER generalizes all exam-ple sentences containing particular concept key entries to accept all the entries of the same key class,and thus builds the natural language template.S PEECH B UILDER also tries to generalize the non-key words in the example sentences so that it can un-derstand a wider variety of user queries than was provided by the developer.Table4contains example actions.Action Examplesidentify what is the forecast for Bostonwhat will the temperature be on TuesdayI would like to know today’s weather in Denver set turn the radio on in the kitchen pleasecan you please turn off the dining room lightsturn on the TV in the living roomTable4:Examples of actions.Put object==(the blue box)location==(on the table)object=(color=blue,item=box),location=(item=table)Put object=(the blue box)location=(on the table)object=(blue box),location=(table)Put the box location==(on the table location==(in the kitchen))item=box,location=(relative=on,item=table,location=(relative=in,room=kitchen))Table5:Examples of strict(==)andﬂattened(=)hierarchy.The system created by S PEECH B UILDER can potentially understand a larger set of queries than are deﬁned by the set of sentence examples,since the examples are converted into a hierarchical-gram for the recognizer,and the understand-ing component backs off to concept spotting when a complete parse is not found.However,the system performs better if givena richer set of examples.4.3.Hierarchical Concept KeysS PEECH B UILDER allows the developer to build a structured grammar when this is desired.This is done by“bracketing”example sentences within the actions–using parentheses to en-force a structure on the way that each particular sentence is parsed.The semantic frame created by the natural language processor reﬂects the hierarchy speciﬁed by the bracketing.To bracket a sentence,the developer encloses the substruc-ture which they wish to separate in parentheses,preceded by a name for the substructure followed by either==or=,depending on whether the developer desires to use strict orﬂattened hier-archy.Strict hierarchy maintains the key/value structure of all concept keys present in the bracketed text.In contrast,ﬂattened hierarchy collapses all internal key/values into a single concept value.Table5shows examples of bracketed example sentences which make use of the hierarchical concept keys.4.4.ResponsesIn addition to conﬁguring ways of asking about information,the developer must also specify how the system will present infor-mation to the user.When a database is provided by the devel-oper,S PEECH B UILDER conﬁgures a generic set of replies for each appropriate database query which could be requested by a user.In addition,responses are generated to handle situations common to all database applications(e.g.,no data matching the input constraints,multiple matches to a user query,too many matches,etc.).Each of these default responses can be modiﬁed by the developer,as desired,to customize the domain.5.Current StatusS PEECH B UILDER has been accessible from within MIT and limited other locations for beta-testing since November2000, and it has been used to create several different domains.The LCSinfo domain,for example,provides access to contact infor-mation for the approximately500faculty,staff,and studentsworking at the MIT Laboratory for Computer Science(LCS) and Artiﬁcial Intelligence Laboratory(including phone num-bers,email addresses,room locations,positions,and group af-ﬁliations)and is able to connect the caller to any of the peo-ple in the database using call bridging.Applications similar to LCSinfo domain are under development by novice develop-ers elsewhere at MIT,as well as at the Space and Naval War-fare Systems Center in San Diego,CA.S PEECH B UILDER has also been used to create a simple appliance application which controls various physical items in an ofﬁce(e.g.lights,cur-tains,projector,television,computer,etc.).This domain is now being used within LCS.S PEECH B UILDER has also been used by members of our group,as well as our industrial partners to create small mixed-initiative database access applications(e.g., schedule information,music queries,stock quotes).In order to allow developers to test out their systems,we have set up a centralized telephone access line,which develop-ers can use to access their deployed domains.In addition,we have provided a local-audio setup so that those developers with access to the GALAXY code distribution can run their domains on their own machines independently of any MIT hardware.6.Ongoing and Future Activities There are many ongoing and future activities which will im-prove the S PEECH B UILDER infrastructure.For example,in or-der to be able to handle more complicated schema for informa-tion access,we plan to introduce the ability to access multiple intersecting database tables from one domain.Currently,all database cells are treated as strings,but in the future we plan to implement functionality for handling Boolean and numerical values as well.Although we have created an initial database query dia-logue manager,we plan to continue to add features which will provide the developer more control for their particular applica-tion.A related area of future research will be to improve the current communication protocol for non-database applications.In areas of speech recognition and understanding,we plan to incorporate our recent work in conﬁdence scoring[11].In the longer term we would like to introduce unsupervised training of acoustic and linguistic models.Currently,developers can only upload transcriptions,which are used as training data for these components.The current conﬁguration of the system defaults to using a commercial speech synthesizer.We would like to provide a facility to developers whereby they can conﬁgure a simple con-catenative speech synthesizer[12].The work we have done thus far has been restricted to American English.However,as we have ongoing research efforts in multilingual conversational systems(e.g.,Japanese, Mandarin Chinese,and Spanish),we have begun to modify the S PEECH B UILDER interface so that multilingual systems can be conﬁgured as well.The systems developed thus far have been prototypes that have not been rigorously evaluated.In order to demonstrate the usefulness of the currently deployed architecture,we plan to conﬁgure a weather information domain within S PEECH-B UILDER,and evaluate at least the speech recognition and understanding components on our standard test sets[1].By demonstrating at least compatible performance with our manu-ally created systems,we hope to show the potential for creating robust,yet rapidly conﬁgurable,spoken dialogue systems.7.SummaryThis paper has provided an overview of our efforts to facilitate the creation of mixed-initiative spoken dialogue systems.If suc-cessful,we believe this research will beneﬁt others by allowing people interested in spoken dialogue systems to rapidly con-ﬁgure applications for their particular interests.We have suc-cessfully deployed two distinct conﬁgurations of the S PEECH-B UILDER utility which connect to applications located on a re-mote web-based server,or to a local database.Several prototype systems have been created with this utility,which are currently in active use.In the near future,we hope to extend the current framework so as to allow for the creation of more complex and powerful spoken dialogue systems.8.AcknowledgementsMany people in the SLS group contributed to this work,includ-ing Issam Bazzi,Scott Cyphers,Ed Filisko,TJ Hazen,Lee Het-herington,Jef Pearlman,Joe Polifroni,Stephanie Seneff,Chao Wang and Jon Yi.9.References[1]V.Zue et al.,“JUPITER:A telephone-based conversationalinterface for weather information,”IEEE Trans.Speech and Audio Proc.,8(1),2000.[2]S.Seneff and J.Polifroni,“Dialogue management inthe MERCURYﬂight reservation system,”Proc.ANLP-NAACL2000,Satellite Workshop,Seattle,2000.[3]S.Sutton,et al.,“Universal Speech Tools:The CSLUToolkit,”Proc.ICSLP,Sydney,1998.[4]S.Seneff,u,and J.Polifroni,“Organization,com-munication,and control in the GALAXY-II conversational system,”Proc.Eurospeech,Budapest,1999.[5]J.Pearlman,“SLS-L ITE:Enabling Spoken LanguageSystems Design for Non-Experts,”M.Eng.Thesis,MIT, Cambridge,2000.[6]J.Glass,T.Hazen,and L.Hetherington,“Real-TimeTelephone-Based Speech Recognition in the JUPITER Do-main,”Proc.ICASSP,Seattle,1999.[7]I.Bazzi and J.Glass,“Modeling out-of-vocabulary wordsfor robust speech recognition,”Proc.ICSLP,Beijing, 2000.[8] A.Black,K.Lenzo and V.Pagel,“Issues in building gen-eral letter to sound rules,”ESCA Speech Synthesis Work-shop,Jenolan Caves,1998.[9]S.Seneff,“TINA:A natural language system for spokenlanguage applications,”Computational Linguistics,18(1), 1992.[10]L.Baptist and S.Seneff,“G ENESIS II:A versatile systemfor language generation in conversational system applica-tions,”Proc.ICSLP,Beijing,2000.[11]T.Hazen,T.Burianek,J.Polifroni,and S.Seneff,“Recog-nition conﬁdence scoring for use in speech understanding systems,”Proc.ISCA ASR Workshop,Paris,2000. [12]J.Yi,J.Glass,and L.Hetherington,“Aﬂexible,scalableﬁnite-state transducer architecture for corpus-based con-catenative speech synthesis,”Proc.ICSLP,Beijing,2000.。