Speech-based interactive information guidance system using question-answering technique
智能聊天机器人英语作文500字
智能聊天机器人英语作文500字英文回答：Intelligent Chatbots: A Transformative Influence on Human Communication.Intelligent chatbots, also known as conversational agents, are rapidly revolutionizing the way we interact with technology and communicate with each other. These advanced software programs are designed to simulate human conversation, providing users with personalized and interactive experiences.Chatbots leverage natural language processing (NLP) and machine learning (ML) algorithms to understand user requests and generate appropriate responses. They can engage in text-based or voice-based conversations, mimicking human speech patterns and offering a wide range of functionalities, from answering questions and providing information to completing tasks and offering emotionalsupport.The use of chatbots has proliferated across various industries, including customer service, healthcare, education, and e-commerce. In customer service, chatbots provide 24/7 support, resolving customer queries and addressing their needs promptly and efficiently. In healthcare, chatbots offer health information, trackpatient data, and provide virtual consultations, making healthcare more accessible and convenient. In education, chatbots enhance learning experiences by delivering personalized feedback, answering student questions, and providing interactive exercises. In e-commerce, chatbots help customers find products, navigate websites, and complete purchases, streamlining the shopping process.Despite their numerous benefits, intelligent chatbots also present certain challenges. One concern is the potential for privacy breaches, as chatbots collect and process user data. Another challenge lies in the development of chatbots that can effectively handle complex or ambiguous user requests. Additionally, the ethicalimplications of using chatbots to replace humaninteractions need to be carefully considered.As technology continues to advance, intelligent chatbots will likely become even more sophisticated and deeply integrated into our lives. They have the potential to transform the way we communicate, access information, and interact with the world around us. However, it is important to approach the development and deployment of chatbots with careful consideration, ensuring that they are used for the benefit of humanity and in a responsible manner.中文回答：智能聊天机器人，革新人类沟通方式的变革性力量。
英语演讲PussinB
Gather information and research on the chosen theme to ensure you have a solid foundation for your arguments and examples
Body language
Non verbal communication
Body language is a form of non verbal communication that can convey messages and emotions For example, a smile can indicate happiness, while crossed arms can indicate defensiveness
Include with a summary that wraps up your argument and leaves the audience with a last impression
Using instances
Use examples that are easily understandable and will reconcile with the audience, drawing parallels to their own experiences and knowledge
Select relevant and competing examples to illustrate and support your points
交互英语第一单元
Preview and preparation of listening materials
Previewing the content
01
Skimming through the material to get a general
idea of the topic and the context
Interactive English is a comprehensive English course that focuses on developing students' communicative competence
It emphasizes on practical language use and resources students to actively participate in classroom interactions
Theme: Introduction to English Language and Culture
Content: This unit introduces students to the basics of English language and culture, including
greetings, introductions, and common everyday expressions
01
02
03
Word 1
Definition
and
example presence
Word 2
Definition
and
example presence
Word 3
Definition
and
example presence
Informative-Speech-说明性演讲
chronological order 时间顺序
to recount the history of an event
topical order 论题顺序
to approach an event from almost any angle or combination of angles
Chengdu from 759 to 765.
IV.Du Fu continued to write works of lasting important
until his death in 770.
Wang Jufang
7
Example spatial order
Specific Purpose: To inform the audience about the design of the Eiffel Tower.
II.The middle section of the tower consists of stairs and elevators that lead to the top.
III.The top section of the tower includes an observation deck with a spectacular view of Pairs.
12
Speeches of Explanation 解释性演讲
inform the audience about subjects that are typically more abstract than the subjects of descriptive or demonstration speeches
词典笔改善了我的学习生活英语作文
词典笔改善了我的学习生活英语作文The dictionary has always been an essential tool for language learners, providing us with the definitions, pronunciations, and usage of words. However, the traditional paper-based dictionary can be cumbersome and inconvenient, especially for students who are constantly on the go. The emergence of dictionary apps on our smartphones and tablets has revolutionized the way we approach language learning. As a student, I have personally experienced the transformative impact of using a dictionary app in my English studies, and I am eager to share how it has significantly improved my learning experience.One of the most significant advantages of using a dictionary app is the immediate accessibility it provides. In the past, whenever I encountered an unfamiliar word while reading or listening, I would have to physically locate the dictionary, flip through the pages, and search for the word. This process was often time-consuming and disruptive to my learning flow. With a dictionary app, I can simply tap on the screen and instantly access the information I need. This seamless integration of the dictionary into my digital devices hasmade the learning process much more efficient and convenient.Furthermore, dictionary apps offer a wealth of features that go beyond the basic definition lookup. Many of these apps include features such as word origin, synonyms, antonyms, and even example sentences. This depth of information has been invaluable in helping me truly understand the nuances and contextual usage of words. For instance, when I come across a word like "elucidate," the dictionary app not only provides the definition but also offers synonyms like "explain" and "clarify," as well as sample sentences demonstrating how the word is used in different contexts. This level of detail has significantly enhanced my vocabulary development and improved my ability to use words accurately and effectively in my own writing and speech.Another remarkable feature of dictionary apps is the integration of audio pronunciations. Being able to hear the correct pronunciation of a word has been a game-changer for me. As a non-native English speaker, I often struggle with the pronunciation of certain words, and the traditional dictionary's phonetic transcriptions can be challenging to interpret. With the audio feature, I can simply tap on the word and listen to the native speaker's pronunciation, allowing me to better understand the sounds and intonations of the English language. This has not only improved my comprehension when listening to others but has also boosted my confidence in speaking and pronouncingwords correctly.Moreover, many dictionary apps offer additional learning tools and resources that have further enhanced my English studies. Some apps include vocabulary quizzes, flashcards, and even language-learning games. These interactive features have made the learning process more engaging and enjoyable, as I can now practice and reinforce my vocabulary knowledge in a fun and interactive way. The gamification aspect of these apps has also helped me stay motivated and dedicated to my language-learning journey, as I can track my progress and achievements through the app's various features.One of the most remarkable aspects of using a dictionary app is its ability to adapt to my personal learning needs and preferences. Many apps allow me to customize the settings, such as the font size, color scheme, and even the language of the interface. This level of personalization has made the app feel like a tailored learning companion, rather than a one-size-fits-all tool. Additionally, the ability to save favorite words, create custom word lists, and access my search history has helped me stay organized and efficient in my language learning.Perhaps the most significant impact of using a dictionary app has been its influence on my overall language proficiency. By having instant access to the definitions, pronunciations, and contextualusage of words, I have been able to expand my vocabulary at a much faster pace. This, in turn, has improved my reading comprehension, writing skills, and overall communication abilities in English. I have noticed a marked improvement in my ability to express myself more eloquently and to understand complex texts and conversations with greater ease.In conclusion, the dictionary app has truly revolutionized my English learning experience. The immediate accessibility, comprehensive information, and interactive features have made the process of vocabulary building and language learning much more efficient, effective, and enjoyable. As a student, I have found that the dictionary app has not only enhanced my academic performance but has also boosted my confidence and overall proficiency in the English language. I highly recommend the use of a dictionary app to any language learner, as it has the potential to transform the way we approach and engage with the learning process.。
SPEECH-ENABLED INFORMATION PROCESSING
专利名称：SPEECH-ENABLED INFORMATIONPROCESSING发明人：EBERMAN, Brian, S.,HUMPHRIES, Jason, J.,VAN DER NEUT, Eric,PATTERSON, Stuart,R.,SPRINGER, Stephen, R.,KOTELLY,Christopher申请号：EP00947562.5申请日：20000720公开号：EP1195042A1公开日：20020410专利内容由知识产权出版社提供摘要：An interactive speech system includes a port configured to receive a call from a user and to provide a communication link between the system and the user, memory having personnel directory information stored therein including indicia of a plurality of people and routing information associated with each person for use in routing the call to a selected one of the plurality of people, the memory also having company information stored therein associated with a company associated with the interactive speech system, and a speech element coupled to the port and the memory and configured to convey first audio information to the port to prompt the user to speak to the system, the speech element also being configured to receive speech from the user through the port, to recognize the speech from the user, and to perform an action based on recognized user's speech, the speech element being further configured to convey second audio information to the port in accordance with the company information stored in the memory.申请人：Speechworks International, Inc.地址：695 Atlantic Avenue,Third Floor Boston, MA 02201 US 国籍：US代理机构：Frost, Alex John更多信息请下载全文后查看。
学生轮流做英文演讲存在的问题和建议作文
学生轮流做英文演讲存在的问题和建议作文全文共3篇示例，供读者参考篇1Title: Issues with Students Taking Turns to Give English Speeches and Suggestions for ImprovementIntroduction:Giving speeches in English is an important skill that many students need to develop, as it can help them improve their communication skills and boost their confidence in public speaking. However, when students are required to take turns giving speeches in English, there are several issues that may arise. In this article, we will discuss the problems that students may face when taking turns giving English speeches and provide some suggestions on how to improve this practice.Issues with Students Taking Turns to Give English Speeches:1. Lack of Individual Attention: When students take turns giving speeches, there may be limited opportunities for teachers to provide individual attention and feedback to each student. This can result in students not receiving the necessary support and guidance to improve their English speaking skills.2. Unequal Skill Levels: Students in the same class may have different levels of English proficiency, which can make it challenging for teachers to accommodate the needs of all students during their speeches. This can lead to some students feeling discouraged or overwhelmed by the expectations set for them.3. Lack of Engagement: Students may become disengaged or lose interest in giving speeches if they feel that it is just a routine task that they have to complete. This can hinder their motivation to improve their English speaking skills and may result in subpar performances during their speeches.Suggestions for Improvement:1. Individualized Feedback: Teachers should provide students with individualized feedback on their speeches, highlighting their strengths and areas for improvement. This can help students understand their progress and encourage them to work on specific aspects of their English speaking skills.2. Grouping Students by Skill Level: Teachers can group students based on their English proficiency levels when assigning speeches, so that each group can receive targeted support and guidance. This can create a more inclusive andsupportive learning environment for students with varying levels of English proficiency.3. Incorporating Interactive Activities: To make giving speeches more engaging and interactive, teachers can incorporate activities such as debates, role plays, and group discussions into the speech-giving process. This can help students practice their English speaking skills in a fun and collaborative way.4. Encouraging Peer Feedback: Teachers can encourage students to provide constructive feedback to their peers during speeches, creating a supportive and collaborative learning environment. This can help students learn from each other's strengths and weaknesses and improve their English speaking skills together.Conclusion:Taking turns to give English speeches can be a beneficial practice for students to improve their communication skills and gain confidence in public speaking. However, it is important for teachers to address the issues that may arise from this practice and implement strategies to improve the overall learning experience for students. By providing individualized feedback, grouping students by skill level, incorporating interactiveactivities, and encouraging peer feedback, teachers can create a more inclusive and supportive learning environment for students to develop their English speaking skills effectively.篇2Title: Problems and Suggestions for Students Taking Turns to Give English SpeechesNowadays, it has become a common practice for students to take turns to give English speeches in class. While this approach does have some benefits, such as improving students' English speaking skills and building their confidence, there are also several problems associated with it. In this essay, we will examine the issues that arise from students giving English speeches in turns, as well as provide some suggestions for maximizing its effectiveness.One of the main problems with students taking turns to give English speeches is the lack of opportunity for individualized feedback. When a large group of students are rotating through the speaker role, it can be difficult for teachers to provide specific and constructive feedback to each student. This can hinder students' progress and prevent them from making meaningful improvements in their English speaking abilities.Another issue is the unequal distribution of speaking opportunities. In a large class where students are required to take turns giving English speeches, some students may end up speaking less frequently than others. This can result in unequal learning outcomes, with some students not getting enough practice to improve their speaking skills.Furthermore, the quality of speeches may suffer when students are constantly rotating through the speaker role. Some students may not put in the necessary effort to prepare for their speeches, leading to lackluster performances and a waste of learning opportunities for both the speaker and the audience.To address these problems, several suggestions can be implemented to make the practice of students taking turns to give English speeches more effective. Firstly, teachers can divide the class into smaller groups and have students give speeches within their own group. This way, teachers can provide more personalized feedback to each student and ensure that everyone has a fair opportunity to speak.Secondly, teachers can set clear expectations and guidelines for students when preparing and delivering their speeches. By establishing clear criteria for evaluation, students will know whatis expected of them and be more motivated to put in the necessary effort to prepare and deliver a quality speech.Lastly, teachers can incorporate more interactive activities into the practice of students giving English speeches. For example, students can participate in group discussions, debates, or role-playing activities to enhance their speaking skills in a more dynamic and engaging way.In conclusion, while the practice of students taking turns to give English speeches can be beneficial for improving their speaking skills, there are several challenges that need to be addressed in order to maximize its effectiveness. By providing individualized feedback, ensuring equal speaking opportunities, setting clear expectations, and incorporating interactive activities, teachers can help students make the most of their English speech practice and achieve better learning outcomes.篇3Title: Issues with Students Taking Turns to Give English Speeches and SuggestionsIntroductionPublic speaking is an important skill that students need to develop in order to succeed in their academic and professionallives. One common practice in schools is to have students take turns giving English speeches in front of their classmates. While this can be a valuable learning experience, there are certain issues that can arise when implementing this practice. In this article, we will discuss some of the problems associated with students taking turns to give English speeches and provide suggestions for addressing these issues.Issues with Students Taking Turns to Give English Speeches1. Lack of Individual AttentionOne of the main problems with students taking turns to give English speeches is the lack of individual attention. In a classroom setting, it can be difficult for teachers to provide feedback and support to each student when they are giving their speeches. This lack of individual attention can hinder students' progress and prevent them from improving their public speaking skills.2. Variation in Skill LevelsAnother issue with students taking turns to give English speeches is the variation in skill levels. Some students may be more confident and articulate speakers, while others may struggle with speaking in front of an audience. This variation inskill levels can make it challenging for teachers to ensure that each student is receiving the appropriate support and guidance they need to improve their public speaking skills.3. Lack of EngagementWhen students take turns giving English speeches, there is a risk of lack of engagement from both the speakers and the audience. Students who are not speaking may become disinterested and distracted, while the audience may tune out if they are hearing multiple speeches in a row. This lack of engagement can diminish the overall effectiveness of the activity and prevent students from fully benefiting from the experience.Suggestions for Addressing These Issues1. Small Group WorkOne way to address the lack of individual attention is to incorporate small group work into the practice of students giving English speeches. By dividing students into smaller groups, teachers can provide more personalized feedback and support to each student. This approach can help students receive the individual attention they need to improve their public speaking skills.2. DifferentiationTo address the variation in skill levels among students, teachers can differentiate their instruction to meet the needs of each student. This may involve providing additional support to students who are struggling with public speaking and challenging more confident students to further develop their skills. By tailoring their instruction to the individual needs of each student, teachers can ensure that all students are making progress in their public speaking abilities.3. Interactive ActivitiesTo increase engagement during English speech activities, teachers can incorporate interactive elements into the practice. This may include peer feedback sessions, group discussions, or interactive presentations. By making the activity more engaging and interactive, students are more likely to stay focused and attentive throughout the process.ConclusionWhile students taking turns to give English speeches can be a valuable learning experience, there are certain issues that need to be addressed to ensure its effectiveness. By implementing the suggestions mentioned above, teachers can help students improve their public speaking skills and make the activity more engaging and beneficial for all students involved. Publicspeaking is an important skill that all students need to develop, and by addressing these issues, teachers can better prepare their students for success in their academic and professional lives.。
基于弗兰德斯互动分析系统的高校体育课师生言语互动行为研究
DOI： 10.16655/ki.2095-2813.2307-1579-6883基于弗兰德斯互动分析系统的高校体育课师生言语互动行为研究(三江学院江苏南京210012)摘要：该文基于弗兰德斯互动分析系统，对高校体育课堂教学中的师生言语互动行为进行了研究。
研究中选择了三江学院8名体育任课教师的24节体育常态课作为课堂观察对象，开展了课堂观察活动，并借助弗兰德斯互动分析系统记录了21 600个师生言语类型编码。
通过对获得的课堂观察编码数据进行统计分析，发现高校体育课堂教学中的师生言语互动行为具有课堂言语结构合理性欠佳、教师言语风格以直接影响为主、学生言语风格以被动发言为主等特点，而上述特点的形成既与高校体育教学自身的特点有关，也与教师的教学理念、教学方法以及教学策略的运用有关。
因此，教师在开展高校体育课堂教学时，应注意通过教学理念的更新迭代、教学方法和教学策略的创新应用，更好地激发学生的体育学练热情，促进高校体育课堂教学中师生言语互动的顺畅、高效进行。
关键词：弗兰德斯互动分析系统 体育课 言语互动行为 高校中图分类号：G80-05文献标识码：A文章编号：2095-2813(2023)27-0039-05 Research on the Speech Interaction Between Teachers andStudents in College Physical Education Classes Based on theFlanders Interaction Analysis SystemCAI Shu(Sanjiang University, Nanjing, Jiangsu Province, 210012 China)Abstract: Based on the Flanders interactive analysis system, this article studies the speech interaction between teachers and students in college physical education classroom teaching. In the study, 24 normal PE classes of 8 PE teachers in Sanjiang University were selected as classroom observation objects, classroom observation activities were carried out, and the speech type codes of 21 600 teachers and students were recorded with the help of the Flanders interactive analysis system. Through the statistical analysis of obtained classroom observation coding data, it is found that teacher-student speech interaction in college physical education classroom teaching has the characteristics of the poor rationality of classroom speech structure, teachers' speech styles centering on direct influences, and students' speech styles centering on passive speeches, and the formation of above characteristics is not only related to the own characteristics of college physical education teaching, but also related to teachers' teaching ideas and the application of teaching methods and teaching strategies. Therefore, when carrying out college physical education classroom teaching, teachers should payattention to better stimulating students' enthusiasm in the learning and practice of physical education by the updatingand iteration of teaching concepts and innovative application of teaching methods and strategies, so as to promote the smooth and efficient speech interaction between teachers and students in college physical education classroom teaching.作者简介：蔡舒（1986—），男，硕士，讲师，研究方向为体育教育、运动训练。
Cultivate students' accuracy and elegance in Engli
Audiovisual material assisted teaching demonstration
Utilize multimedia resources such as videos, audio clips, and podcasts to demonstrate proper enrollment
Encourage students to use their imagination and creativity to develop their own scenarios and dialogues, emphasizing clear and accurate promotion Provide feedback and guidance on students' performance, highlighting areas for improvement and training their efforts and progress
Pronunciation is the basis for effective communication: Accurate and Elegant Pronunciation can improve the intelligence and fluency of Spoken English, then enhance communication effectiveness
in describing English sounds
Explanation of voters and consonant sounds in English, including short and long voters, diphthongs, and consonant
Academic Presentation-Unit1听力答案
His famous PowerPoint presentation has attracted the public, with its meticulously researched content and clear style.
"The only vice president ever to mock his stiff image by (imitating) a wax-museum figure, Gore turns out to be the best professor you never had -- easygoing, knowledgeable and funny." —Rolling Stone
How to Make A Presentation Part3
Keys
• • • • • • • • • • • (1) introduce yourself (2) your name (3) give the talk (4) title (5) be talking about (6) how long (7) ask questions (8) yourself (9) outlined (10) transition (11) moving on to • (12) you help your audience to understand you • (13) to remember that you have your are having a conversation with them • (14) giving a lecture • (15) reading from the script • (16) eye contact • (17) scratching your head • (18) blowing your nose • (19) sticking your hands in the pocket
中学生优秀英文演讲课件
metaphors to resonate and
attract
the
audience's
attention
Example 2: Cultural Exchange Theme Speech
Speech topic
Crossing cultural divide and promoting exchange and mutual learning
Grammar correctness
The sentence structure is clear, the grammar is accurate, and there are no obvious grammar errors.
Clear pronunciation
The speaker has accurate pronunciation, clear pronunciation, natural intonation, and is easy for the audience to understand.
Speech content
Introduce the differences and commonalities between different cultures, explore the significance
and value of cultural exchange
Presentation skills
04
CHAPTER
Analysis of speech examples
Example 1: Environmental Protection Theme Speech
Speech Topic
Protecting the Environment, Starting from Me
Consistency, Regularity, and Frequency Effects
L ANGUAGE AND L INGUISTICS 6.1:75-107, 20052005-0-006-001-000145-1Consistency, Regularity, and Frequency Effectsin Naming Chinese CharactersChia-Ying Lee12, Jie-Li Tsai2, Erica Chung-I Su2,Ovid J. L. Tzeng12 and Daisy L. Hung121Academia Sinica2National Yang-Ming UniversityThree experiments in naming Chinese characters are presented here to address the relationships between character frequency, consistency, and regularityeffects in Chinese character naming. Significant interactions between characterconsistency and frequency were found across the three experiments, regardless ofwhether the phonetic radical of the phonogram is a legitimate character in its ownright or not. These findings suggest that the phonological information embeddedin Chinese characters has an influence upon the naming process of Chinesecharacters. Furthermore, phonetic radicals exist as computation units mainlybecause they are structures occurring systematically within Chinese characters,not because they can function as recognized, freestanding characters. On the otherhand, the significant interaction between regularity and consistency found in thefirst experiment suggests that these two factors affect Chinese character naming indifferent ways. These findings are accounted for within interactive activationframeworks and a connectionist model.Key words: frequency, consistency, regularity, naming task1. IntroductionMany efforts towards developing models of pronunciation for alphabetic writing systems have focused on the effects in naming tasks exerted by two properties of words: (1) Frequency (how often a word is encountered), and (2) consistency or regularity (whether the pronunciation has a predictable spelling-to-sound correspondence). Behavioral studies have shown a robust interaction between these two properties. That is, the regularity or consistency of spelling-to-sound correspondences often has little impact on naming high frequency words. However, for low frequency words, regularity or consistency words usually contributes to faster and more accurate naming than for exception words (Seidenberg et al. 1984, Seidenberg 1985, Taraban & McClelland 1987, but see also Jared et al. 1990, 1997). At least two models, the dual-route modelChia-Ying Lee, Jie-Li Tsai, Erica Chung-I Su, Ovid J. L. Tzeng, and Daisy L. Hungand the parallel-distributed processing model (PDP), are proposed to explain this interaction. These models differ from one another in terms of the assumptions they make concerning the mappings between orthography and phonology and concerning the number of mechanisms responsible for the orthography-to-phonology transformation. 1.1 Dual-route and PDP modelsThe dual-route model uses the notion of “regularity” to define mappings between orthography and phonology. Broadly speaking, a written English word is regular if its pronunciation follows the grapheme-to-phoneme correspondence rules (or GPC rules) of the written language (Venezky 1970); and a word is an exception if its pronunciation deviates from those rules. According to the traditional dual-route model, the “assembled-route” operates by means of GPC rules. This process will produce only “regular” pronunciations and will do so regardless of the frequency or familiarity of the letter string (Coltheart 1978, 1983). In contrast, the addressed-route operates by paired-association. It not only compensates for the mistakes that GPC rules make regarding exception words, but also ensures that the pronunciation system is sensitive to frequency. The interaction of frequency and regularity is explained by the relative finishing time assumption. For low-frequency exception words, it is assumed that the assembly route will produce its incorrect pronunciation in about the same interval of time as the addressed route produces its correct one. In such a case, two candidate pronunciations arrive at the response-generation mechanism for programming articulation at approximately the same time, and this creates a conflict. To resolve this conflict delays the onset of pronunciation. It can lead to errors if pronunciation is initiated before the conflict is fully resolved in favor of the correct phonology.On the other hand, the analogy-based account proposed by Glushko (1979) and the connectionist account proposed by Seidenberg and McClelland (1989) adopted the term “consistency” to describe mappings between orthography and phonology. Spelling-sound consistency was defined with respect to the orthographic body and the phonological rime (Glushko 1979, Taraban & McClelland 1987, Seidenberg & McClelland 1989, Van Orden et al. 1990). A consistent English word (e.g. WADE) is one that has a word-body (-ADE) pronounced in the same way for the entire set of orthographic neighbors. An inconsistent word (e.g., WAVE) has among its neighbors at least one exception word (e.g., HAVE). The definition of consistency is independent of the definition of regularity. Thus a word can be, like WAVE, both regular, because it follows the GPC rules, and inconsistent, because it does not rhyme with all its neighbors. Moreover, a word like WADE that not only follows the GPC rules, but also rhymes with all its neighbors is both regular and consistent.76Consistency, Regularity, and Frequency Effects in Naming Chinese Characters Glushko (1979) argued that, relative to regularity, consistency provides a better account for word naming latency data because he found that regular but inconsistent words, like WAVE, take longer to pronounce than regular and consistent words like WADE. If regularity is an undifferentiated category, both consistent and inconsistent groups of regular words should be named with the same latency. In addition, pseudowords like TAVE, which resemble exception words, take longer to read aloud than pseudowords like TAZE, which resemble regular words (Glushko 1979). The dual-route model can predict neither of these findings. Therefore, the analogy account claimed that a candidate set of word or subword representations will be activated by perceptual input and the subsequent synthesis process is responsible for the pronunciation. The interaction of frequency and consistency is explained by the relative size and the compatibility of phonological realization of the candidate set. The low frequency exception words activate many neighbors and these neighbors include different and mutually incompatible phonological realizations. The resulting conflict takes time to resolve.As for the PDP model, the pronunciations are determined within a subsymbolic connectionist network, which connects input orthography to output phonology (Van Orden et al. 1990). The network learns from exposure to particular words. The factor having the largest impact on the model’s performance with a given word is the number of exposures to the word itself during training (i.e., word frequency). The high frequency words have been encountered many times in the past. The connection of a high frequency word’s orthography and phonology will be quite strong. The settling time of a high frequency word will be relatively fast. The other factor having impact is input to the model in terms of other similarly or non-similarly spelled words; that is, consistency. For the pronunciation of a consistent word, there will be no conflict among the phonological features that become activated. The settling time will also be relatively fast. Both frequency and consistency influence the settling time. On the other hand, the low frequency exception words have not been learned well enough to settle rapidly, by virtue of the sheer strength of their connections. Further, they activate too many incompatible phonological features to settle rapidly by virtue of their consistency. Therefore, the interaction of frequency and consistency is explained as a product of the network’s learning history. In general, as the number of exposures to a given word decrease, the naming performance of that word depends more on the properties of similarly spelled word neighbors.77Chia-Ying Lee, Jie-Li Tsai, Erica Chung-I Su, Ovid J. L. Tzeng, and Daisy L. Hung1.2 The characteristics of Chinese orthographyChinese is characterized as being a logographic writing system with deep orthography. The correspondence between orthography and phonology in Chinese is more arbitrary than in the writing systems with shallow orthographies, like Serbo-Croatian or English. Some researchers believe that the mapping between orthography and phonology in Chinese is quite opaque. Therefore, the pronunciation of each Chinese character must be learned individually, making the assembled route from orthography to phonology unavailable (Paap & Noel 1991). However, if we carefully observe the evolution of writing systems, we find that the relation between script and meaning has become increasingly abstract, while the relation between script and speech has become increasingly clear. DeFrancis (1989) made detailed analyses of various kinds of writing systems from the perspective of their historical development and claimed that any fully developed writing system is speech-based, even though the way speech is represented in the script varies from one language to another (DeFrancis 1989). Furthermore, he emphasized that Chinese orthography is also a speech-based script since more than 85% of Chinese characters are phonograms, in which a part of the character carries clues to its pronunciation.Chinese writing was possibly pictographic in origin (Hung & Tzeng 1981). However, owing to difficulties in forming characters to represent abstract concepts, phonograms were invented. Phonograms usually are complex characters, typically composed of a semantic radical and a phonetic radical. The semantic radical usually gives a hint to the character’s meaning, whereas the phonetic radical provides clues to the pronunciation of the character. For example: the character 媽ma (mother) is written with a semantic radical 女to indicate the meaning of “female”, and a phonetic radical 馬ma to represent the sound of the whole character. Due to the historical sound changes and the influence of dialects, many phonetic radicals of the compound character lost the function of providing clues to pronunciation. Among modern Chinese characters, less than 48% of the complex characters have exactly the same pronunciations as their phonetic radicals (Zhou 1978). However, the relationship between orthography and phonology is far from null in Chinese. It is still worth asking whether readers can use their knowledge of the relationship between orthography and phonology in naming.1.3 Definition of regularity and consistency in Chinese charactersSince there are no GPC rules in Chinese, it is impossible to classify Chinese characters as regular or irregular according to whether they follow the GPC rules.78Consistency, Regularity, and Frequency Effects in Naming Chinese Characters Previous studies have tried to describe the mappings between Chinese orthography and phonology in two different ways. The first one is to define the “regularity” as whether the sound of a character is identical with that of its phonetic radical, ignoring tonal difference (Lien 1985, Fang et al. 1986, Hue 1992). For example, 油you is regular because it sounds the same as its phonetic radical 由you. An irregular or exceptional character would be the character whose pronunciation deviates from that of its phonetic radical. For example, 抽chou is irregular because it sounds different from its phonetic radical 由you.The second way to describe the mappings of Chinese orthography and phonology is the concept of consistency. Fang et al. (1986) considered a character to be consistent if all the characters in its set of orthographic neighbors, which share the same phonetic radical, have the same pronunciation; otherwise, it was inconsistent. In addition to this dichotomous distinction of consistency, Fang et al. (1986) introduced a method to estimate the consistency value of a character, which is similar to the degree of consistency defined by Jared et al. (1990) to capture the magnitude of the consistency effect. The consistency value is defined as the relative size of a phonological group within a given activation group. For example, there are twelve characters that include the phonetic radical 由you. Among these, 迪 and 笛are pronounced as di and have a consistency value of 0.17 (i.e., 2/12). Therefore, each character can be assigned a gradient consistency value in addition to the dichotomous category of consistency.1.4 The role of regularity and consistency in Chinese character namingSeveral studies have addressed the role of regularity and consistency in naming Chinese characters. Seidenberg (1985) found that regular characters were named faster than frequency-matched non-phonograms (simple characters without a phonetic radical) when the characters were of low frequency. This result showed that regular, complex characters could be named more efficiently than simple characters with no phonetic radical. However, this is not a typical regularity effect. Fang et al. (1986) asked participants to name regular and irregular characters. The regular characters could be subdivided into two types, consistent and inconsistent. Their results showed an effect due to consistency, but none due to regularity. Specifically, regular-consistent characters were named faster than regular-inconsistent characters, but the regular-inconsistent characters were not named faster than the irregular-inconsistent characters. A similar trend was observed by Lien (1985). However, the stimuli in both these studies were restricted to high frequency characters. Hue (1992) further manipulated the character frequency and found both regularity and consistency effects for low frequency characters.79Chia-Ying Lee, Jie-Li Tsai, Erica Chung-I Su, Ovid J. L. Tzeng, and Daisy L. Hung80 These results indicate that phonological information contained in Chinesecharacters is used in character pronunciation. However, some controversy remains. First, both Fang et al. (1986) and Lien (1985) reported a consistency effect for high frequency characters, whereas Hue (1992) did not. On the other hand, Hue (1992) reported the regularity effect, but neither Fang et al. (1986) nor Lien (1985) did so. Therefore, an issue that needs further clarification is whether the consistency and regularity effect can be found in naming high frequency characters. Second, although consistency may be calculated as a continuous value, most previous studies define it as a dichotomous variable in order to contrast the effects of complete consistency with any degree of inconsistency. Fang et al. (1985) found that the pronunciation latencies of simple characters, which serve as phonetic radicals in compound characters, were also affected by their inconsistency values. However, whether the degree of consistency affects naming of Chinese complex characters, or phonograms, and whether this interacts with frequency and regularity remain to be seen.2. Experiment 1The first purpose of Experiment 1 was to investigate whether consistency and regularity effects can be found in naming high frequency characters. Four types of characters were included in this experiment. They were (1) consistent and regular, (2) inconsistent and regular, (3) inconsistent and irregular, and (4) the non-phonograms. The second purpose of this experiment was to examine the relationship between regularity and consistency. We manipulated the relative consistency value within inconsistent/regular and inconsistent/irregular conditions to address this specific question.2.1 Method2.1.1 ParticipantsThe participants were eighteen undergraduate students recruited from a pool of participants at Yang-Ming University. All were native speakers of Chinese. Their participation partially fulfilled their course requirements.2.1.2 ApparatusAll stimuli were presented and all responses were collected using a Pentium 166 MMX personal computer with a voice-key relay attached through the computer’s printer port. A microphone was placed on a stand and attached to a voice-key delay. AConsistency, Regularity, and Frequency Effects in Naming Chinese Characters81separate microphone was attached to a tape recorder and was used to record the participants’ naming responses.2.1.3 Materials and designOne hundred and sixty Chinese characters were selected for this experiment. (These are listed in Appendix 1.) Half were high frequency characters (more than 150 occurrences per 10 million) and half were low frequency characters (less than 80 occurrences per 10 million). According to the definition of consistency and regularity in this study, each of the two frequency groups was divided into four subsets by character type: (1) consistent/regular, (2) inconsistent/regular, (3) inconsistent/irregular, and(4) non-phonograms. Subsets of characters within a frequency group were matched for frequency according to the Mandarin Chinese Character Frequency List (Chinese Knowledge Information Processing Group 1995). Each subset contained twenty characters. All of the characters in the set of non-phonograms were single characters or compound characters without phonetic radicals. Although some non-phonograms do function as phonetic radicals in phonograms, no such non-phonograms were selected for use in this study. The criteria for each condition and illustrative examples are shown in Table 1.Table 1: Examples and characteristics of the characters in different frequencygroups and character types for Experiment 1Character typeConsistent /regular Inconsistent /regular Inconsistent /irregular Non- phonogramHigh frequencyExample 距 誠 媒 傘Pronunciation ju4 cheng2 mei2 san3 Meaning distance honest medium umbrella Frequency 985 1096 1224 1030 Consistency value 1.00 0.46 0.42 * Low frequency Character 胰 膛 儕 吝Pronunciation yi2 tang2 chai2 lin4 Meaning pancreas chest a class stingy Frequency 39 33 28 42 Consistency value 1.00 0.53 0.39 * Note Frequencies were calculated using the technical report of the Mandarin Chinese CharacterFrequency List (1995). Character frequencies greater than 1500 were truncated to 1500. Asterisk (*) indicates no consistency value.Chia-Ying Lee, Jie-Li Tsai, Erica Chung-I Su, Ovid J. L. Tzeng, and Daisy L. Hung82 For investigating whether consistency level affects naming performance, wesubdivided the inconsistent/regular character and inconsistent/irregular character setsinto relatively high and relatively low consistency subsets. Each subset included ten characters. The consistency values of the relatively high group ranged from 0.50 to 0.89.Those in the relatively low group ranged from 0.10 to 0.47. The criteria for each condition and illustrative examples are shown in Table 2.Table 2: Examples and characteristics of the characters differing in regularity,consistency, and frequency for Experiment 1Regularity Regular IrregularConsistency High Low High LowHighfrequencyExample 誠週媒抽Pronunciation cheng2 zhou mei2 chouMeaning honestweekmediumtopump Frequency 1188 1003 1308 1139 Consistencyvalue 0.64 0.28 0.65 0.20 LowfrequencyExample 膛桅儕犢Pronunciation yi2wei2chai2du2 Meaning pancreasmastaclasscalf Frequency 32 34 27 28 Consistencyvalue 0.73 0.33 0.58 0.21Note Frequencies were calculated using the technical report of the Mandarin Chinese Character Frequency List (1995). Character frequencies greater than 1500 were truncated to 1500.2.1.4 ProcedureParticipants were individually tested in a small room. They sat in front of the PC ata distance of approximately 60 cm. Before exposure to the experimental stimulus items,they underwent ten practice runs, so as to familiarize them with the procedure and sothat the experimenter could adjust the sensitivity of the voice-key delay.During the experimental period, one hundred and sixty characters were presentedto each participant in random order. Each trial began with a visual presentation of afixation point for 1000 ms, accompanied by a 500 Hz beep signal for 300 ms. Then atarget character was presented in the center of the screen for the participant to name. All participants were instructed to name each character as quickly and as accurately as possible. The target character remained on the screen until the participant responded oruntil an interval of 3000 ms had expired. Articulation onset latencies were recorded bymeans of the voice-key delay. Naming latencies were discarded from trials on whichConsistency, Regularity, and Frequency Effects in Naming Chinese Characters there were pronunciation errors or voice-key triggering errors due to environmental noise. The pronunciation errors were recorded by the experimenter. Uncertainties regarding naming responses were resolved by listening to the audiotape. Naming latencies longer than 1500 ms were considered null responses by the program, and those 200 ms or shorter were regarded by the program as voice-key triggering errors. After the response or the expiration of the 3000 ms interval, a blank screen was displayed until the experimenter recorded the correctness of the response. The participants could take a break after each set of 40 experimental trials or after any trial if necessary.2.2 Results2.2.1 Analysis of frequency and character typeThere were two variables for this analysis: frequency (high vs. low) and character type (consistent/regular, inconsistent/regular, inconsistent/irregular, and non-phonogram). These were treated as within-subject variables in the analysis by subjects (F1) and between-item variables in the analysis by items (F2). Analyses of variance (ANOVA) were performed on latency data and accuracy data. The mean reaction time and percent error rate for the each condition are presented in Figure 1.Figure 1: Mean naming latencies and error rates for conditions with different frequencies and character types for Experiment 183Chia-Ying Lee, Jie-Li Tsai, Erica Chung-I Su, Ovid J. L. Tzeng, and Daisy L. Hung84 Participants named high-frequency characters significantly faster than low- frequency characters, F 1(1,17)=255.52, p<.001, MSe=238418, and F 2(1,152)=126.20, p<.001, MSe=357873, and more accurately, F 1(1,17)=50.29, p<.001, MSe=0.289, and F 2 (1,152)=31.52, p<.001, MSe=0.325. The main effects of character type were significant both in the latency data, F 1(3,51)=22.79, p<.001, MSe=30177, and F 2(3,152)=17.22, p<.001, MSe=48830, and in the accuracy data, F 1(3,17)=32.015, p<.001, MSe=0.115, and F 2(3,152)=12.489, p<.001, MSe=0.129. The interaction between frequency and consistency was also significant in the latency data, F 1(3,51)=31.11, p<.001, MSe=24096, and F 2(3,152) =12.13, p<.001, MSe=34388, and in the accuracy data, F 1(3,51)=27.36, p<.001, MSe=0.095, and F 2(3,152)=10.24, p<.001, MSe=0.106.For the high frequency condition, the simple main effect of character type was significant in the latency data in the analysis by participant, F 1(3,102)=4.317, p<.01, MSe=4529, but not in the analysis by item, F 2<1. None of those effects achieved significance in the accuracy data, Fs<1. Post hoc comparisons of the latency data from the analysis by participant were conducted to see if there were consistency and regularity effects in naming high frequency characters. A significant consistency effect showed that participants named consistent/regular characters faster than the inconsistent/regular ones, F 1(1,102)=5.69, p<.05. There was no difference in naming latency between the inconsistent/regular and inconsistent/irregular character sets (F 1<1), nor between the non-phonograms and consistent/regular character sets (F 1<1). However, the non-phonograms were named much faster than the inconsistent/regular characters, F 1 (1,102)=8.22, p<.01, and faster than the inconsistent/irregular characters, F 1(1,102)=6.16, p<.05.For the low frequency condition, the simple main effects of character type were significant both in the latency data, F 1(3,102)=47.41, p<.001, MSe=49744, and F 2(3,152) =27.6, p<.001, MSe=78266, and in the accuracy data, F 1(3,102)=59.23, p<.001, MSe=0.208, and F 2(3,152)=22.644, p<.001, MSe=0.233. The post hoc comparison between consistent/regular and inconsistent/regular characters was marginally significant in the analysis of latency data, F 1(1,102)=2.28, p=.13, and F 2(1,152)=3.31, p=.07, and was significant in the accuracy data, F 1(1,102)=4.20, p<.05, and F 2(1,152)=1.74, p=.19. The inconsistent/regular characters were named faster than the inconsistent/irregular characters, F 1(1,102)=80.38, p<.001, and F 2(1,152)=45.62, p<.001, and more accurately, F 1 (1,102)=99.43, p<.001 and F 2(1,152)=45.62, p<.001. On the other hand, the non-phonograms were named more slowly than were the consistent/regular characters, F 1(1,102)=36.05, p<.001, and F 2(1,152)=18.94, p<.001, and less accurately, F 1(1,102)=11.66, p<.01, and F 2(1,152)=4.82, p<.05. The non-phonograms were also named more slowly than the inconsistent/regular characters, F 1(1,102)=20.01, p<.001, and F 2(1,152)=6.42, p<.05, but there was no difference in naming accuracy. However, the non-phonogramsConsistency, Regularity, and Frequency Effects in Naming Chinese Characters were named faster than the inconsistent/irregular characters, F1(1,102)=19.99, p<.001, and F2(1,152)=17.81, p<.001, and more accurately, F1(1,102)=74.06, p<.001, and F2(1,152) =30.63, p<.001.2.2.2 Analysis of frequency, regularity, and the consistency levelOne further analysis investigated the relationships between regularity and consistency. Both the inconsistent/regular and inconsistent/irregular character groups, both their high and low frequency conditions, were split into two groups based on relative consistency. They yielded three variables for this analysis: frequency (high vs. low), regularity (regular vs. irregular), and consistency level (high vs. low). They were treated as within-subject variables in the analysis by participants (F1) and between-item variables in the analysis by items (F2). ANOVAs were performed on the latency and accuracy data. The mean reaction time and error rate for each condition are presented in Figure 2.Figure 2: Mean naming latencies and error rates for conditions with different frequencies and character types for Experiment 1Chia-Ying Lee, Jie-Li Tsai, Erica Chung-I Su, Ovid J. L. Tzeng, and Daisy L. HungOf interest here are the relationships between regularity and consistency. For the latency data, a three-way interaction among frequency, regularity, and consistency level was only marginally significant, F1(1,17)=4.004, p=.06, MSe=15034, and F2(1,72)=3.147, p=.07, MSe=12040. There was a significant two way interaction between regularity and consistency in the analysis by participants, F1(1,17)=7.921, p<.05, MSe=14542, but not by item, F2(1,72)=2.766, p=.12, MSe=10574. The simple main effect showed that the consistency level was significant only when a character was irregular, F1(1,34)=9.837, p<.001, MSe=17143. An irregular, high consistency character was named faster than an irregular, low consistency one.For the accuracy data, the three-way interaction was significant in the analysis by participants, F1(1,17)=14.167, p<0.001, MSe=0.062, but not in the analysis by items, F2 (1,72)=2.664, p=0.12, MSe=0.035. The analysis of simple interaction showed that theinteraction between the consistency level and regularity was significant in the low frequency condition, F1(1,34)=22.91, p<0.001, MSe=0.133, but not in the high frequency condition (Fs<1). The simple main effects of consistency were significant for low frequency characters, both regular, F1(1,68)=137.01, p<0.001, MSe=0.751, and irregular, F1(1,17)=22.35, p<0.001, MSe=0.122. Consistency effects could be found in naming both low frequency regular characters and low frequency irregular characters.2.3 DiscussionExperiment 1 replicated the interaction between frequency and character types and yielded several additional interesting results. First, the regularity effect obtained by contrasting irregular/inconsistent and regular/inconsistent characters was restricted to the low frequency characters. This is consistent with Hue (1992). Second, the comparison between the naming latencies of consistent/regular and inconsistent/regular characters showed significant consistency effects in naming both high frequency characters (28 ms) and low frequency characters (17 ms). The consistency effect found in the high frequency characters replicates the results obtained by Fang et al. (1986) and Lien (1985) (both of whom used only high frequency characters as stimuli), but not by Hue (1992). Third, relative to the non-phonograms, the naming of the regular or consistent phonograms is faster and more accurate, whereas naming an irregular and inconsistent phonogram is slower and less accurate than naming a non-phonogram. These results support the claim that phonological information embedded in Chinese characters is used in the naming process.Furthermore, a significant interaction between the consistency level and regularity was found in naming low frequency characters. This indicates that, in addition to character frequency, a working model of Chinese character pronunciation should address。
speech
第一张Hello everyone. It is my pleasure standing here to show our presentation. The topic is Network security. Our group consists of 6 members, namely 1~6. Before we start, I would like to introduce one conception to you all. That is—click第二张(接上页)The definition of network security. 照着念。
So let’s come to our main content of the presentation.第三张There are three parts in our team work. Firstly, Three kinds of problems in network securities. This includes individuals who encounter with network securities, as well as enterprises and countries. Secondly, we will discuss what is the cause of these problems. After that some advises of our own thought will be put forward. Now let’s begin with the first part.第四张1.Look at this picture. When registering hotmails or other mailboxes, one necessity is to sign your names or your ID numbers. Therefore, other people will have a chance to embezzle our personal information. Then we will easily fall in network security incidents.2.See the next picture. According to xinhua news agency, half of netizens have encountered network security incidents. This was partly due to other people's moral failings, and also their own Internet safety awareness’insufficiency.第五张Then we talk about the enterprise. we separate it into two parts which include internal and external.1.With the development of enterprises, one new interactive operation mode which combines company headquarters, local branch, mobile office staff and so on gradually formed. Network connection security between institutions and headquarters directly affects the enterprise high-efficient operation. So, dealing with headquarters and branches, mobile office staff information sharing safety, enterprises must ensure the timely information sharing, as well as preventing confidential leakage.2.Now have a look at the other picture. Here the two cows are compared to two companies. Based on its own interests consideration, they peer the commercial confidential information through the Internet, or through the malicious means to attack the other party's network system, so as to achieve the purpose of grabbing market share.第六张Now as you all see, this lovely boy, no, not a boy. I dare to say this is really a genius for his work is so brilliant. This reminds us of our childhood when we looked at some cartoons, where often emerged one very clever, but also funny doctor who did a lot of unbelievable inventions and would always say: I am a genius! I am a genius! Now come back to the picture. His name is Gary McKinnon, coming from London, UK. In 2001, McKinnon hacked into the U.S. military, the National Aeronautics and Space Administration (NASA), including a number of sensitive sectors of the computer network, resulting in about 100 million dollars in direct losses. McKinnon installed software to "fully control the computer." He downloaded a lot of these computers sensitive information, including U.S. Navy ships manufacture and supply of arms and other aspects of information, which are extremely destructive to the USA government.第七张Then come the main causes of these annoying problems. First, Security vulnerabilities and system door. Second, Artificial malicious attacks. Third, Viruses, worms, trojans and spyware. And last, Netizens safety awareness.第八张According to all those mentioned above, some security strategies must be taken. As we can see on this page. The first one ising the vulnerability scanning technique: with important network equipment in risk assessment, we can guarantee information system as far as possible in the optimal condition operation.2. Using various security technologies to construct the defense system, mainly include:2.1 the firewall technology firewall's main purpose is to intercept need not flow in the network layer access control, firewall help improve the Lord of the whole system security.2.2 the authentication provide identity based authentication, and in various authentication mechanism can choose to use.2.3 multi-level enterprise-level anti-virus system adopting multi-layer enterprise-level anti-virus system to achieve full protection.2.4 network real-time monitoring use the intrusion detection system to hosts and network monitoring, and further enhance the ability of foreign attack networks.3. Real-time response and recovery establish and perfect the system of safety management, improve the network attack such as real-time response and recovery capacity.And last, Establish delaminated management and all levels of safety management center.第九张And lastly, we come to this page, the conclusion. In the middle lies network security, and above people’s cooperation, right an intelligent policy and below consistent practices. In a word, security is everybody’s business, and only with everyone’s cooperation, an intelligent policy, and consistent practices, will it be achievable.。
基于深度学习语音分离技术的研究现状与进展
第42卷第6期自动化学报Vol.42,No.6 2016年6月ACTA AUTOMATICA SINICA June,2016基于深度学习语音分离技术的研究现状与进展刘文举1聂帅1梁山1张学良2摘要现阶段,语音交互技术日益在现实生活中得到广泛的应用,然而,由于干扰的存在,现实环境中的语音交互技术远没有达到令人满意的程度.针对加性噪音的语音分离技术是提高语音交互性能的有效途径,几十年来,全世界范围内的许多研究者为此投入了巨大的努力,提出了很多实用的方法.特别是近年来,由于深度学习研究的兴起,基于深度学习的语音分离技术日益得到了广泛关注和重视,显露出了相当光明的应用前景,逐渐成为语音分离中一个新的研究趋势.目前已有很多基于深度学习的语音分离方法被提出,但是,对于深度学习语音分离技术一直以来都缺乏一个系统的分析和总结,不同方法之间的联系和区分也很少被研究.针对这个问题,本文试图对语音分离的主要流程和整体框架进行细致的分析和总结,从特征、模型以及目标三个方面对现有的前沿研究进展进行全面而深入的综述,最后对语音分离技术进行展望.关键词神经网络,语音分离,计算听觉场景分析,机器学习引用格式刘文举,聂帅,梁山,张学良.基于深度学习语音分离技术的研究现状与进展.自动化学报,2016,42(6):819−833 DOI10.16383/j.aas.2016.c150734Deep Learning Based Speech Separation Technology and Its Developments LIU Wen-Ju1NIE Shuai1LIANG Shan1ZHANG Xue-Liang2Abstract Nowadays,speech interaction technology has been widely used in our daily life.However,due to the interfer-ences,the performances of speech interaction systems in real-world environments are far from being satisfactory.Speech separation technology has been proven to be an effective way to improve the performance of speech interaction in noisy environments.To this end,decades of efforts have been devoted to speech separation.There have been many methods proposed and a lot of success achieved.Especially with the rise of deep learning,deep learning-based speech separation has been proposed and extensively studied,which has been shown considerable promise and become a main research line. So far,there have been many deep learning-based speech separation methods proposed.However,there is little systematic analysis and summary on the deep learning-based speech separation technology.We try to give a detail analysis and summary on the general procedures and components of speech separation in this regard.Moreover,we survey a wide range of supervised speech separation techniques from three aspects:1)features,2)targets,3)models.Andfinally we give some views on its developments.Key words Neural network,speech separation,computational auditory scene analysis,machine learningCitation Liu Wen-Ju,Nie Shuai,Liang Shan,Zhang Xue-Liang.Deep learning based speech separation technology and its developments.Acta Automatica Sinica,2016,42(6):819−833现实环境中,感兴趣的语音信号通常会被噪音干扰,严重损害了语音的可懂度,降低了语音识别的性能.针对噪音,前端语音分离技术是最常用的方法之一.一个好的前端语音分离模块能够极大地提高语音的可懂度和自动语音识别系统的识别性能[1−6].然而,在真实环境中,语音分离技术的性能远没有达收稿日期2015-11-04录用日期2016-04-01Manuscript received November4,2015;accepted April1,2016国家自然科学基金(61573357,61503382,61403370,61273267,911 20303,61365006)资助Supported by National Natural Science Foundation of China (61573357,61503382,61403370,61273267,91120303,61365006)本文责任编委柯登峰Recommended by Associate Editor KE Deng-Feng1.中国科学院自动化研究所模式识别国家重点实验室北京1001902.内蒙古大学计算机系呼和浩特0100211.National Laboratory of Patten Recognition,Institute of Automation,Chinese Academy of Sciences,Beijing1001902. College of Computer Science,Inner Mongolia University,Huhhot 010021到令人满意的程度,特别是在非平稳噪音和单声道的情况下,语音分离依然面临着巨大的挑战.本文重点探讨单声道条件下语音分离技术.几十年来,单声道条件下的语音分离问题被广泛地研究.从信号处理的角度来看,许多方法提出估计噪音的功率谱或者理想维纳滤波器,比如谱减法[7]和维纳滤波法[8−9].其中维纳滤波是最小均方误差意义下分离纯净语音的最优滤波器[9].在假定语音和噪音的先验分布的条件下,给定带噪语音,它能推断出语音的谱系数.基于信号处理的方法通常假设噪音是平稳的或者是慢变的[10].在满足假设条件的情况下,这些方法能够取得比较好的分离性能.然而,在现实情况下,这些假设条件通常很难满足,其分离性能会严重地下降,特别在低信噪比条件下,这些方法通常会失效[9].相比于信号处理的方法,基于模型的方法利用混合前的纯净信号分别构建语音820自动化学报42卷和噪音的模型,例如文献[11−13],在低信噪比的情况下取得了重要的性能提升.但是基于模型的方法严重依赖于事先训练的语音和噪音模型,对于不匹配的语音或者噪音,其性能通常会严重下降.在基于模型的语音分离方法中,非负矩阵分解是常用的建模方法,它能挖掘非负数据中的局部基表示,目前已被广泛地应用到语音分离中[14−15].然而非负矩阵分解是一个浅层的线性模型,很难挖掘语音数据中复杂的非线性结构.另外,非负矩阵分解的推断过程非常费时,很难应用到实际应用中.计算听觉场景分析是另一个重要的语音分离技术,它试图模拟人耳对声音的处理过程来解决语音分离问题[16].计算听觉场景分析的基本计算目标是估计一个理想二值掩蔽,根据人耳的听觉掩蔽来实现语音的分离.相对于其他语音分离的方法,计算听觉场景分析对噪音没有任何假设,具有更好的泛化性能.然而,计算听觉场景分析严重依赖于语音的基音检测,在噪音的干扰下,语音的基音检测是非常困难的.另外,由于缺乏谐波结构,计算听觉场景分析很难处理语音中的清音成分.语音分离旨在从被干扰的语音信号中分离出有用的信号,这个过程能够很自然地表达成一个监督性学习问题[17−20].一个典型的监督性语音分离系统通常通过监督性学习算法,例如深度神经网络,学习一个从带噪特征到分离目标(例如理想掩蔽或者感兴趣语音的幅度谱)的映射函数[17].最近,监督性语音分离得到了研究者的广泛关注,取得了巨大的成功.作为一个新的研究趋势,相对于传统的语音增强技术[9],监督性语音分离不需要声源的空间方位信息,且对噪音的统计特性没有任何限制,在单声道,非平稳噪声和低信噪比的条件下显示出了明显的优势和相当光明的研究前景[21−23].从监督性学习的角度来看,监督性语音分离主要涉及特征、模型和目标三个方面的内容.语音分离系统通常利用时频分解技术从带噪语音中提取时频域特征,常用的时频分解技术有短时傅里叶变换(Short-time Fourier transform,STFT)[24]和Gammatone听觉滤波模型[25].相应地,语音分离特征可以分为傅里叶变换域特征和Gammatone滤波变换域特征.Wang和Chen等在文献[26−27]中系统地总结和分析了Gammatone滤波变换域特征,提出了一些列组合特征和多分辨率特征.而Mohammadiha、Xu、Weninger、Le Roux、Huang 等使用傅里叶幅度谱或者傅里叶对数幅度谱作为语音分离的输入特征[14,18,20,23,28−29].从建模单元来区分,语音分离的特征又可分为时频单元级别的特征和帧级别的特征.时频单元级别的特征从一个时频单元的信号中提取,帧级别的特征从一帧信号中提取,早期,由于模型学习能力的限制,监督性语音分离方法通常对时频单元进行建模,因此使用时频单元级别的特征,例如文献[1]和文献[30−34].现阶段,监督性语音分离主要使用帧级别的特征[17−21,23,35−36].监督性语音分离系统的学习模型主要分为浅层模型和深层模型.早期的监督性语音分离系统主要使用浅层模型,比如高斯混合模型(Gaussian mixture model,GMM)[1]、支持向量机(Support vector machine,SVM)[26,30,32]和非负矩阵分解(Nonnegative matrix factorization, NMF)[14].然而,语音信号具有明显的时空结构和非线性关系,浅层结构在挖掘这些非线性结构信息的能力上非常有限.而深层模型由于其多层次的非线性处理结构,非常擅长于挖掘数据中的结构信息,能够自动提取抽象化的特征表示,因此,近年来,深层模型被广泛地应用到语音和图像处理中,并取得了巨大的成功[37].以深度神经网络(Deep neural network,DNN)为代表的深度学习[37]是深层模型的典型代表,目前已被广泛应用到语音分离中[5,18,20,22,29,38−39].最近,Le Roux、Hershey和Hsu等将NMF扩展成深层结构并应用到语音分离中,取得了巨大的性能提升[23,40−41],在语音分离中显示了巨大的研究前景,日益得到人们的重视.理想时频掩蔽和目标语音的幅度谱是监督性语音分离的常用目标,如果不考虑相位的影响,利用估计的掩蔽或者幅度谱能够合成目标语音波形,实验证明利用这种方法分离的语音能够显著地抑制噪音[42−43],提高语音的可懂度和语音识别系统的性能[38,44−49].但是,最近的一些研究显示,相位信息对于语音的感知质量是重要的[50].为此,一些语音分离方法开始关注相位的估计,并取得了分离性能的提升[51−52].为了将语音的相位信息考虑到语音分离中,Williamson等将浮值掩蔽扩展到复数域,提出复数域的掩蔽目标,该目标在基于深度神经网络的语音分离系统中显著地提高了分离语音的感知质量[53].语音分离作为一个重要的研究领域,几十年来,受到国内外研究者的广泛关注和重视.近年来,监督性语音分离技术取得了重要的研究进展,特别是深度学习的应用,极大地促进了语音分离的发展.然而,对监督性语音分离方法一直以来缺乏一个系统的分析和总结,尽管有一些综述性的工作被提出,但是它们往往局限于其中的一个方面,例如,Wang等在文献[17]中侧重于监督性语音分离的目标分析,而在文献[26]中主要比较了监督性语音分离的特征,并没有一个整体的总结和分析,同时也没有对这些工作的相互联系以及区别进行研究.本文从监督性语音分离涉及到的特征、模型和目标三个主要方面6期刘文举等:基于深度学习语音分离技术的研究现状与进展821对语音分离的一般流程和整体框架进行了详细的介绍、归纳和总结.以此希望为该领域的研究及应用提供一个参考.本文的组织结构如下:第1节概述了语音分离的主要流程和整体框架;第2∼5节分别介绍了语音分离中的时频分解、特征、目标、模型等关键模块;最后,对全文进行了总结和展望,并从建模单元、目标和训练模型三个方面对监督性语音分离方法进行了比较和分析.1系统结构图1给出了语音分离的一般性结构框图,主要分为5个模块:1)时频分解,通过信号处理的方法(听觉滤波器组或者短时傅里叶变换)将输入的时域信号分解成二维的时频信号表示.2)特征提取,提取帧级别或者时频单元级别的听觉特征,比如,短时傅里叶变换谱(FFT-magnitude)、短时傅里叶变换对数谱(FFT-log)、Amplitude modulation spec-trogram(AMS)、Relative spectral transform and perceptual linear prediction(RASTA-PLP)、Mel-frequency cepstral coefficients(MFCC)、Pitch-based features以及Multi-resolution cochleagram (MRCG).3)分离目标,利用估计的分离目标以及混合信号合成目标语音的波形信号.针对语音分离的不同应用特点,例如针对语音识别,语音分离在分离语音的过程中侧重减少语音畸变和尽可能地保留语音成分.针对语音通讯,语音分离侧重于提高分离语音的可懂度和感知质量.常用的语音分离目标主要分为时频掩蔽的目标、目标语音幅度谱估计的目标和隐式时频掩蔽目标,时频掩蔽目标训练一个模型来估计一个理想时频掩蔽,使得估计的掩蔽和理想掩蔽尽可能相似;目标语音幅度谱估计的方法训练一个模型来估计目标语音的幅度谱,使得估计的幅度谱与目标语音的幅度谱尽可能相似;隐式时频掩蔽目标将时频掩蔽技术融合到实际应用的模型中,用来增强语音特征或估计目标语音,隐式掩蔽并不直接估计理想掩蔽,而是作为中间的一个计算过程来得到最终学习的目标,隐式掩蔽作为一个确定性的计算过程,并没有参数需要学习,最终的目标误差通过隐式掩蔽的传导来更新模型参数.4)模型训练,利用大量的输入输出训练对通过机器学习算法学习一个从带噪特征到分离目标的映射函数,应用于语音分离的学习模型大致可分为浅层模型(GMM,SVM,NMF)和深层模型(DNN,DSN, CNN,RNN,LSTM,Deep NMF).5)波形合成,利用估计的分离目标以及混合信号,通过逆变换(逆傅里叶变换或者逆Gammatone滤波)获得目标语音的波形信号.图1监督性语音分离系统的结构框图Fig.1A block diagram of the supervised speechseparation system2时频分解时频分解是整个系统的前端处理模块,通过时频分解,输入的一维时域信号能够被分解成二维的时频信号.常用的时频分解方法包括短时傅里叶变换[24]和Gammatone听觉滤波模型[25].假设w(t)=w(−t)是一个实对称窗函数, X(t,f)是一维时域信号x(k)在第t时间帧、第f个频段的短时傅里叶变换系数,则X(t,f)=+∞−∞x(k)w(k−t)exp(−j2πfk)d k(1)对应的傅里叶能量幅度谱p x(t,f)为p x(t,f)=|X(t,f)|(2)其中,|·|表示复数域的取模操作.为了简化符号表示,用向量p∈R F×1+表示时间帧为t的幅度谱,这里F是傅里叶变换的频带数.短时傅里叶变换是完备而稳定的[54],可以通过短时傅里叶逆变换(Inverse short-time Fourier transform,ISTFT)从X(t,f)精确重构x(k).也就是说,可以通过估计目标语音的短时傅里叶变换系数来实现语音的分离或者增强,用ˆY s(t,f)来表示估计的目标语音的短时傅里叶变换系数,那么目标语音的波形ˆs(k)可以通过ISTFT计算ˆs(k)=+∞−∞+∞−∞ˆYs(k)w(k−t)exp(j2πfk)d f d k(3)如果不考虑相位的影响,语音分离过程可以转换为目标语音幅度谱的估计问题,一旦估计出目标822自动化学报42卷语音的幅度谱ˆys ∈R F×1+,联合混合语音的相位,通过ISTFT,能得到目标语音的估计波形ˆs(k)[17].Gammatone听觉滤波使用一组听觉滤波器g(t)对输入信号进行滤波,得到一组滤波输出G(k,f).滤波器组的冲击响应为g(t)=t l−1exp(−2πbt)cos(2πft),t≥00,其他(4)其中,滤波器阶数l=4,b为等效矩形带宽(Equiv-alent rectangle bandwidth,ERB),f为滤波器的中心频率,Gammatone滤波器组的中心频率沿对数频率轴等间隔地分布在[80Hz,5kHz].等效矩形带宽与中心频率一般满足式(5),可以看出随着中心频率的增加,滤波器带宽加宽.ERB(f)=24.7(0.0043f+1.0)(5)对于4阶的Gammatone滤波器,Patterson 等[25]给出了带宽的计算公式b=1.093ERB(f)(6)然后,采用交叠分段的方法,以20ms为帧长,10ms 为偏移量对每一个频率通道的滤波响应做分帧加窗处理.得到输入信号的时频域表示,即时频单元.在计算听觉场景分析系统中,时频单元被认为是处理的最小单位,用T-F表示.通过计算时频单元内的内毛细胞输出(或者听觉滤波器输出)能量,就得到了听觉谱(Cochleagram),本文用GF(t,f)表示时间帧t频率为f的时频单元T-F的听觉能量.3特征语音分离能够被表达成一个学习问题,对于机器学习问题,特征提取是至关重要的步骤,提取好的特征能够极大地提高语音分离的性能.从特征提取的基本单位来看,主要分为时频单元级别的特征和帧级别的特征.时频单元级别的特征是从一个时频单元的信号中提取特征,这种级别的特征粒度更细,能够关注到更加微小的细节,但是缺乏对语音的全局性和整体性的描述,无法获取到语音的时空结构和时序相关性,另外,一个时频单元的信号,很难表征可感知的语音特性(例如,音素).时频单元级别的特征主要用于早期以时频单元为建模单元的语音分离系统中,例如,文献[1,26,30−32],这些系统孤立地看待每个时频单元,在每一个频带上训练二值分类器,判断每一个频带上的时频单元被语音主导还是被噪音主导;帧级别的特征是从一帧信号中提取的,这种级别的特征粒度更大,能够抓住语音的时空结构,特别是语音的频带相关性,具有更好的全局性和整体性,具有明显的语音感知特性.帧级别的特征主要用于以帧为建模单元的语音分离系统中,这些系统一般输入几帧上下文帧级别特征,直接预测整帧的分离目标,例如,文献[17−20,27,35].近年来,随着语音分离研究的深入,已有许多听觉特征被提出并应用到语音分离中,取得了很好的分离性能.下面,我们简要地总结几种常用的听觉特征.1)梅尔倒谱系数(Mel-frequency cepstral co-efficient,MFCC).为了计算MFCC,输入信号进行20ms帧长和10ms帧移的分帧操作,然后使用一个汉明窗进行加窗处理,利用STFT计算能量谱,再将能量谱转化到梅尔域,最后,经过对数操作和离散余弦变换(Discrete cosine transform,DCT)并联合一阶和二阶差分特征得到39维的MFCC.2)PLP(Perceptual linear prediction).PLP 能够尽可能消除说话人的差异而保留重要的共振峰结构,一般认为是与语音内容相关的特征,被广泛应用到语音识别中.和语音识别一样,我们使用12阶的线性预测模型,得到13维的PLP特征.3)RASTA-PLP(Relative spectral trans-form PLP).RASTA-PLP引入了RASTA滤波到PLP[55],相对于PLP特征,RASTA-PLP对噪音更加鲁棒,常用于鲁棒性语音识别.和PLP一样,我们计算13维的RASTA-PLP特征.4)GFCC(Gammatone frequency cepstral co-efficient).GFCC特征是通过Gammatone听觉滤波得到的.我们对每一个Gammatone滤波输出按照100Hz的采样频率进行采样.得到的采样通过立方根操作进行幅度压制.最后,通过DCT得到GFCC.根据文献[56]的建议,一般提取31维的GFCC特征.5)GF(Gammatone feature).GF特征的提取方法和GFCC类似,只是不需要DCT步骤.一般提取64维的GF特征.6)AMS(Amplitude modulation spectro-gram).为了计算AMS特征,输入信号进行半波整流,然后进行四分之一抽样,抽样后的信号按照32ms帧长和10ms帧移进行分帧,通过汉明窗加窗处理,利用STFT得到信号的二维表示,并计算STFT幅度谱,最后利用15个中心频率均匀分布在15.6∼400Hz的三角窗,得到15维的AMS特征.7)基于基音的特征(Pitch-based feature).基于基音的特征是时频单元级别的特征,需要对每一个时频单元计算基音特征.这些特征包含时频单元被目标语音主导的可能性.我们计算输入信号的Cochleagram,然后对每一个时频单元计算6维的基音特征,详细的计算方法可以参考文献[26,57].8)MRCG(Multi-resolution cochleagram).6期刘文举等:基于深度学习语音分离技术的研究现状与进展823MRCG的提取是基于语音信号的Cochleagram表示的.通过Gammatone滤波和加窗分帧处理,我们能得到语音信号的Cochleagram表示,然后通过以下步骤可以计算MRCG.步骤1.给定输入信号,计算64通道的Cochle-agram,CG1,对每一个时频单元取对数操作.步骤2.同样地,用200ms的帧长和10ms的帧移计算CG2.步骤3.使用一个长为11时间帧和宽为11频带的方形窗对CG1进行平滑,得到CG3.步骤4.和CG3的计算类似,使用23×23的方形窗对CG1进行平滑,得到CG4.步骤5.串联CG1,CG2,CG3和CG4得到一个64×4的向量,即为MRCG.MRCG是一种多分辨率的特征,既有关注细节的高分辨率特征,又有把握全局性的低分辨率特征.9)傅里叶幅度谱(FFT-magnitude).输入的时域信号进行分帧处理,然后对每帧信号进行STFT,得到STFT系数,然后对STFT进行取模操作即得到STFT幅度谱.10)傅里叶对数幅度谱(FFT-log-magnitude). STFT对数幅度谱是在STFT幅度谱的基础上取对数操作得到的,主要目的是凸显信号中的高频成分.以上介绍的听觉特征是语音分离的主要特征,这些特征之间既存在互补性又存在冗余性.研究显示,对于单个独立特征,GFCC和RASTA-PLP分别是噪音匹配条件和噪音不匹配条件下的最好特征[26].基音反映了语音的固有属性,基于基音的特征对语音分离具有重要作用,很多研究显示基于基音的特征和其他特征进行组合都会显著地提高语音分离的性能,而且基于基音的特征非常鲁棒,对于不匹配的听觉条件具有很好的泛化性能.然而,在噪音条件下,准确地估计语音的基音是非常困难,又因为缺乏谐波结构,基于基音的特征仅能用于浊音段的语音分离,而无法处理清音段,因此在实际应用中,基于基音的特征很少应用到语音分离中[26],实际上,语音分离和基音提取是一个“鸡生蛋,蛋生鸡”的问题,它们之间相互促进而又相互依赖.针对这一问题,Zhang等巧妙地将基音提取和语音分离融合到深度堆叠网络(Deep stacking network,DSN)中,同时提高了语音分离和基音提取的性能[34].相对于基音特征,AMS同时具有清音和浊音的特性,能够同时用于浊音段和清音段的语音分离,然而, AMS的泛化性能较差[58].针对各个特征之间的不同特性,Wang等利用Group Lasso的特征选择方法得到AMS+RASTA−PLP+MFCC的最优特征组合[26],这个组合特征在各种测试条件下取得了稳定的语音分离性能而且显著地优于单独的特征,成为早期语音分离系统最常用的特征.在低信噪比条件下,特征提取对于语音分离至关重要,相对于其他特征或者组合特征,Chen等提取的多分辨率特征MRCG表现了明显的优势[27],逐渐取代AMS+RASTA−PLP+MFCC的组合特征成为语音分离最常用的特征之一.在傅里叶变换域条件下,FFT-magnitude或FFT-log-magnitude 是最常用的语音分离特征,由于高频能量较小,相对于FFT-magnitude,FFT-log-magnitude能够凸显高频成分,但是,一些研究表明,在语音分离中, FFT-magnitude要略好于FFT-log-magnitude[28].语音分离发展到现阶段,MRCG和FFT-magnitude分别成为Gammtone域和傅里叶变换域下最主流的语音分离特征.此外,为了抓住信号中的短时变化特征,一般还会计算特征的一阶差分和二阶差分,同时,为了抓住更多的信息,通常输入特征会扩展上下文帧.Chen等还提出使用ARMA模型(Auto-regressive and moving average model)对特征进行平滑处理,来进一步提高语音分离性能[27].4目标语音分离有许多重要的应用,总结起来主要有两个方面:1)以人耳作为目标受体,提高人耳对带噪语音的可懂度和感知质量,比如应用于语音通讯;2)以机器作为目标受体,提高机器对带噪语音的识别准确率,例如应用于语音识别.对于这两个主要的语音分离目标,它们存在许多密切的联系,例如,以提高带噪语音的可懂度和感知质量为目标的语音分离系统通常可以作为语音识别的前端处理模块,能够显著地提高语音识别的性能[59],Weninger等指出语音分离系统的信号失真比(Signal-to-distortion ratio,SDR)和语音识别的字错误率(Word error rate,WER)有明显的相关性[5],Weng等将多说话人分离应用于语音识别中也显著地提高了识别性能[6].尽管如此,它们之间仍然存在许多差别,以提高语音的可懂度和感知质量为目标的语音分离系统侧重于去除混合语音中的噪音成分,往往会导致比较严重的语音畸变,而以提高语音识别准确率为目标的语音分离系统更多地关注语音成分,在语音分离过程中尽可能保留语音成分,避免语音畸变.针对语音分离两个主要目标,许多具体的学习目标被提出,常用的分离目标大致可以分为三类:时频掩蔽、语音幅度谱估计和隐式时频掩蔽.其中时频掩蔽和语音幅度谱估计的目标被证明能显著地抑制噪音,提高语音的可懂度和感知质量[17−18].而隐式时频掩蔽通常将掩蔽技术融入到实际应用模型中,时频掩蔽作为中间处理过程来提高其他目标的性能,例如语音识别[5,60]、目标语音波形的估计[21].。
speechsdk
Speech SDKThe Speech SDK is a comprehensive software development kit provided by Microsoft that enables developers to incorporate speech recognition and synthesis capabilities into their applications. With the Speech SDK, developers can easily integrate speech functionality into their applications, making them more engaging and interactive.Key FeaturesThe Speech SDK offers a range of features to support speech recognition and synthesis. Some of the key features include: Speech RecognitionSpeech recognition allows applications to convert spoken language into written text. The Speech SDK provides several powerful capabilities for speech recognition:•Continuous Recognition: The Speech SDK supports continuous speech recognition, allowing applications totranscribe speech in real-time. This is useful forapplications such as transcription services, voice assistants, and more.•Customization: Developers can customize the speech recognition models provided by the Speech SDK to improve accuracy for specific domains or use cases.Customization can include adding and training a customlanguage model or adapting the existing models for specific tasks.•Multiple Recognition Modes: The Speech SDK supports different recognition modes, including interactive mode, dictation mode, and conversation mode, providing developers with flexibility in designing speech-enabledapplications.Speech SynthesisSpeech synthesis is the process of converting written text into spoken words. The Speech SDK offers advanced capabilities for speech synthesis:•High-Quality Voices: The Speech SDK includes a wide range of high-quality voices in different languages and dialects. These voices sound natural and can be customized to suit specific requirements.•SSML Support: The Speech SDK supports Speech Synthesis Markup Language (SSML). SSML allowsdevelopers to add additional instructions such as prosody, emphasis, and pronunciation to enhance the synthesizedspeech.•Custom Voice Creation: Developers can utilize the Speech SDK to create custom voices by training with a set of specific voice data. This makes it possible to generateunique voices for specific applications or scenarios.Getting StartedTo start using the Speech SDK, follow these steps:1.Installation: Download and install the Speech SDK from the official Microsoft website. The SDK is available for various platforms, including Windows, macOS, Linux, Android, and iOS.2.API Key: Obtain an API key from the Azure portal. The API key is required to authenticate your application’s requests to the Speech SDK service.3.Import the SDK: Include the Speech SDK in your application project by adding the appropriate library or package dependency.4.Code Integration: Integrate the Speech SDK into your application code using the software development language of your choice. The Speech SDK provides APIs and libraries for various programming languages, such as C#, Python, JavaScript, and Java.5.Configure Speech Recognition or Synthesis: Set up the speech recognition or synthesis functionality according to your application requirements. This may include selecting the appropriate recognition mode, choosing the desired voice for synthesis, or setting other parameters.6.Testing and Debugging: Test your application to ensure the speech functionality is working as expected. The Speech SDK provides debugging and logging tools to help you identify and fix any issues that arise.7.Deployment: Once testing is complete, package your application for deployment to your target platforms.Ensure that the necessary libraries and dependencies are included in the deployment package.ResourcesThe following resources are available to assist developers in utilizing the Speech SDK effectively:•Documentation: The official Microsoftdocumentation provides detailed information on theSpeech SDK, including API references, code samples,tutorials, and best practices.•Support: Microsoft offers support services to assist developers with any questions or issues encountered while using the Speech SDK. Support options include onlineforums, community discussions, and support fromMicrosoft engineers.•Sample Code: The Speech SDK comes with a wide range of sample code, illustrating various speechrecognition and synthesis scenarios. These samples canserve as a starting point for developers to understand and implement the SDK’s capabilities.ConclusionThe Speech SDK is a powerful development kit that empowers developers to incorporate speech recognition and synthesis capabilities into their applications. By using the Speech SDK, developers can create innovative and interactive applications that bring the power of speech to their users. Whether it’s enabling voice commands, building transcriptionservices, or creating unique voice experiences, the Speech SDK provides the foundation for creating cutting-edge speech-enabled applications.。
互动语言学 英文
Interactive Linguistics is a dynamic and interdisciplinary field that investigates the complex nature of human communication, focusing on how language users interact in real-time conversations to create meaning. It encompasses various aspects such as conversational analysis, pragmatics, sociolinguistics, and psycholinguistics, providing a comprehensive understanding of the intricate mechanisms involved in verbal exchanges. This paper aims to delve into the core tenets of interactive linguistics from multiple angles, offering a high-quality, in-depth exploration.**Conversational Analysis**At its heart, interactive linguistics emphasizes the study of conversation as a primary data source. Conversational Analysis (CA) meticulously examines turn-taking, repair mechanisms, and sequential organization to reveal how speakers collaboratively construct meaning. For instance, the concept of 'turn-taking' highlights the intricate dance of dialogue where participants anticipate, initiate, and conclude their turns, adhering to culturally ingrained norms. The 'repair mechanism,' another key element, underscores how speakers correct themselves or seek clarification when misunderstandings occur, thereby maintaining coherence and continuity in the discourse.**Pragmatic Dimension**Interactive Linguistics also embraces pragmatics, the study of language use in context. It delves into implicatures, presuppositions, and speech acts, exploring how meaning transcends the literal words spoken. In an interactive setting, the pragmatic competence of speakers enables them to interpret indirect meanings, negotiate intentions, and adjust their language according to social contexts. For example, speakers often use politeness strategies to mitigate threats to face, demonstrating how linguistic choices are deeply intertwined with social relationships and interactional goals.**Sociolinguistic Aspects**A sociolinguistic lens is integral to interactive linguistics, examining how societal factors influence language use. This includes power dynamics,identity construction, and the role of language varieties. Speakers adapt their language based on factors like status, familiarity, and cultural norms, shaping and being shaped by the interactive space. For instance, code-switching – the alternation between languages or dialects –can serve as a powerful communicative tool reflecting the speaker's social affiliations and situational demands.**Psycholinguistic Insights**The psychological aspect of language processing and production is also crucial in interactive linguistics. Researchers explore cognitive processes involved in comprehension, production, and negotiation of meaning during interaction. This involves mental planning of utterances, anticipation of interlocutors’ responses, and rapid adjustments based on feedback. Moreover, the brain's capacity for processing information in real-time conversations, including managing overlapping speech and dealing with ambiguity, adds depth to our understanding of interactive linguistic phenomena.**Technological Advancements and Future Directions**In the digital era, interactive linguistics has expanded its horizons to include computer-mediated communication and AI-human interactions. This development has led to new research areas such as chatbot design, natural language processing, and multimodal communication studies. These advancements not only enrich our knowledge of human interaction but also contribute to the development of more sophisticated AI systems capable of simulating and responding to human-like language.In conclusion, Interactive Linguistics, through its multifaceted approach, provides a comprehensive framework for understanding the rich tapestry of human communication. By synthesizing insights from conversational analysis, pragmatics, sociolinguistics, psycholinguistics, and technology, it offers a high-quality analytical lens through which we can decipher the complexities inherent in everyday verbal exchanges. Its continued evolution promises to deepen our grasp of language’s role in constructing and maintaining our socialworld.This summary, while substantial, serves as a mere introduction to the vast and nuanced field of interactive linguistics. Each of these subfields warrants extensive discussion and detailed analysis beyond this scope, yet together they form a harmonious symphony that elucidates the intricacies of human interaction through language. With ongoing research and technological innovation, interactive linguistics will undoubtedly continue to shed light on the dynamic relationship between language and human cognition within social contexts.。
英语语音教学语音从娃娃抓起超好讲义
English is spoken with various accents and dialects across the world. Understanding the differences in pronunciation between dialects and accents can help students better understand and adapt to different speech patterns.
Intonation and rhythm
Intonation
Intonation refers to the rise and fall of pitch in speech, which conveys important information about the speaker's attitude, emphasis, and intention. Understanding intonation patterns is crucial for effective communication.
Importance of learning the IPA
Learning the IPA is essential for students of English phonetics because it provides a standardized way to represent and transcribe speech sounds, allowing for more accurate analysis and description of pronunciation.
Creating a language environment
【论文】人机交互论文中英对照
【关键字】论文An Agenda for Human-Computer Interaction Research:Interaction Styles INTRODUCTIONThe bottleneck in improving the usefulness of interactive systems increasingly lies not in performing the processing task itself but in communicating requests and results between the system and its user. The best leverage for progress in this area therefore now lies at the user interface, rather than the system internals. Faster, more natural, and more convenient means for users and computers to exchange information are needed. On the user's side, interactive system technology is constrained by the nature of human communication organs and abilities; on the computer side, it is con-strained only by input/output devices and methods that we can invent. The challenge before us is to design new devices and types of dialogues that better fit and exploit the communication-relevant characteristics of humans.The problem of human-computer interaction can be viewed as two powerful information processors (human and computer) attempting to communicate with each other via a narrow-bandwidth, highly constrained interface . Research in this area attempts to increase the useful bandwidth across that interface. Faster, more natural––and particularly less sequential, more parallel––modes of user-computer communication will help remove this bottleneck.一项研究议程人机交互：交互方式简介日益改善的障碍交互系统的有用性在于在执行请求不予处理任务本身，而是在沟通和结果之间的系统和它的用户。
ai 英语单词造句
ai 英语单词造句AI英语单词造句近年来，随着人工智能（AI）的迅速发展，英语学习也面临着许多新的挑战和机会。
AI英语单词造句技术的应用为英语学习者提供了更加智能化、个性化的学习方式。
下面是一些关于AI英语单词造句的例子，展示了AI技术在英语学习中的应用。
1. Digitalization (数码化)The digitalization of the education sector has revolutionized the way students learn English.2. Virtual (虚拟)Virtual reality technology creates an immersive environment for English language learners.3. Interactive (互动的)The interactive English learning platform allows students to practice vocabulary through real-time conversations.4. Pronunciation (发音)AI provides instant feedback on pronunciation, helping learners improve their English speaking skills.5. Grammar (语法)AI-powered grammar checkers can identify and correct grammatical errors in English sentences.6. Vocabulary (词汇)The AI-based vocabulary builder suggests contextually appropriate words to enhance English language skills.7. Translation (翻译)AI translation tools facilitate the understanding of English texts by providing accurate translations.8. Language Learning App (语言学习应用)The AI-driven language learning app offers personalized English lessons based on the user's proficiency and learning goals.9. Speech Recognition (语音识别)AI speech recognition technology enables English learners to practice speaking and receive feedback on their pronunciation.10. Adaptive Learning (自适应学习)The AI-powered adaptive learning system tailors English lessons to each student's individual needs and learning pace.11. Fluency (流利)With the help of AI, learners can improve their English fluency through interactive speaking exercises.12. Natural Language Processing (自然语言处理)AI's natural language processing capabilities facilitate English learners' comprehension and interpretation of written texts.13. Vocabulary Expansion (词汇扩展)AI algorithms recommend additional English words for learners to expand their vocabulary.14. Contextual Understanding (语境理解)AI tools analyze the context of English sentences to help learners understand and use words appropriately.15. Sentiment Analysis (情感分析)AI-powered sentiment analysis helps learners understand the nuances of emotions conveyed in English texts.16. Comprehension (理解)AI reading comprehension tools assist English learners in understanding complex texts and answering related questions.17. Error Correction (错误纠正)AI systems can detect and correct errors in English writing, providing learners with helpful feedback.18. Cultural Context (文化背景)AI-powered English learning materials incorporate cultural context to enhance learners' understanding of the language.在AI技术的帮助下，英语学习者可以更高效、便捷地提升自己的英语能力。
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SPEECH-BASED INTERACTIVE INFORMATION GUIDANCE SYSTEM USING QUESTION-ANSWERING TECHNIQUETeruhisa Misu Tatsuya KawaharaSchool of Informatics,Kyoto University,Kyoto606-8501,JapanABSTRACTThis paper addresses an interactive framework for informa-tion navigation based on document knowledge base.In con-ventional audio guidance systems,such as those deployed in museums,the informationƀow is one-way and the content isſxed.In order to make an interactive guidance system, we propose the application of question-answering(QA)tech-niques.Since users tend to use anaphoric expressions in suc-cessive questions,we investigate appropriate handling of con-textual information based on topic detection,together with the effect of using N-best information in ASR output.Moreover, we apply the QA technique to generation of system-initiative information recommendation.A navigation system on Ky-oto city information was implemented.Effectiveness of the proposed techniques was conſrmed through aſeld trial by a number of real novice users.Index Terms—spoken dialogue system,question-answering,information guidance1.INTRODUCTIONThe target of spoken dialogue systems is being extended from simple databases such asƀight information to general doc-uments including manuals[1]and newspaper articles[2].In such systems,the automatic speech recognition(ASR)result of the user utterance is matched against a set of target docu-ments using the vector space model,and documents with high matching scores are presented to the user.We have devel-oped“Speech Dialogue Navigator”,which can retrieve infor-mation from a large-scale software support knowledge base (KB)with a spoken dialogue interface[3].Most of these types of dialogue systems assume that a dis-play is available as an output device,and thus a list of matched documents can be presented.However,this is not the case when only speech interface is available,for example,using phones and audio guidance systems.Considering user’s eas-iness of comprehension,the amount of the content presented at a time should be limited.But simply summarizing the re-trieved document may cause a loss of the important portion the user intended to know or may be interested in.Actually, in the conventional audio guidance systems deployed in mu-seums and sightseeing spots,users cannot ask questions on the missed portion.We therefore propose a more interactive scheme by incorporating the question-answering(QA)tech-nique to follow up the initial query enabling random access to any part of the document.There are some problems with QA in such situations.One important issue is contextual analysis.In dialogue session, users tend to make questions that include anaphoric expres-sions.In these cases,it is impossible to extract the correct answer using the current question only.(For example,“When was it built?”makes no sense with this sentence alone.) In many conventional database query tasks,this problem is solved by using the task domain knowledge such as the se-mantic slots of the backend database[4,5].Whereas the majority of the conventional QA tasks,such as TREC QA Track[6],have dealt with independent questions that have re-spective answer for each,there have been only a few works that addressed successive questions[7].But they have basi-cally hand-crafted questions rather than collecting real dia-logues.In this work,we address the QA task in a real inter-active guidance system using a topic tracking mechanism.Furthermore,we introduce generation of system-initiative information recommendation.In spoken dialogue systems, users often have a difſculty in making queries because of unsureness of the list of information the system possesses. Moreover,the system-initiative guidance is also useful in nav-igating users in the tasks without deſnite goal,such as sight-seeing guidance.In order to make an interactive guidance, we propose the application of the QA technique to generate system-initiative recommendations.Based on the above concepts,we have designed and im-plemented an interactive guidance system of“Dialogue Nav-igator for Kyoto City”,and conducted aſeld trial for about three months.Key evaluation results of the QA function are presented in this paper.2.FRAMEWORK OF THE SYSTEMThe proposed guidance system prepares two modes of a user-initiative retrieval/QA mode(pull-mode)and a system-initiative recommendation mode(push-mode),and switches them according to the user’s state.When a query or a question is uttered by a user,the system switches to the retrieval/QA mode and generate a respective response.When the sys-tem detects the silence of the user,it switches to the system-initiative recommendation mode and presents information re-Kyoto city.The KBs of this domain are Wikipedia1docu-ments concerning Kyoto and the ofſcial tourist information of Kyoto city.Table1lists the size of these KBs.ER-INITIATIVE INFORMATION RETRIEV ALAND QUESTION-ANSWERINGThe user utterances are classiſed into two categories.One is an information query,such as“Please explain Golden Pavil-ion”.For such queries,the system retrieves from the KB by section unit,and the document section with the largest match-ing score is presented to the user.The other is a question, such as“When was it built?”.The system extracts the sen-tence from the KB that includes the answer to the question and presents it to the user.This procedure is shown in Fig-ure2.3.1.Contextaul Analysis based on Topic DetectionIn dialogue systems,incorporation of contextual information is an important issue to generate a meaningful query for re-trieval.As the deterministic anaphora resolution[8]is not easy and always error-prone,and stochastic matching is used in information retrieval,we adopt a strategy to concatenate contextual information or keywords in the user’s previous ut-terances to generate a query.The simplest way is to use all utterances of the current user.However,it might add inap-propriate context because the topic might have been changed in the session.We therefore determine the length of context (number of previous utterances)used for retrieval by tracking the topic of the dialogue.Whereas De Boni[9]proposed semantic similarity tech-niques to detect contextual questions with typed-input,it would be difſcult to adopt such an approach for dialogue sys-tems with speech-input,in which queries tend to be short and reference words are often omitted2.As a topic,we therefore 1/2especially in Japanese 3.2.Document RetrievalWe adopt an orthodox vector space model to calculate a matching score(degree of similarity)between user query and the document in the KB.That is,the vector of the document is made based on the occurrence counts of nouns in the doc-ument by section unit.The vector for the user query is also made by merging N-best hypotheses of the ASR result of the current utterance and previous utterances about the current topic as a context.We also use the ASR conſdence measure (CM)as a weight for the nouns.Matching score is calculated by the product of these two vectors.For the retrieved document,a summary is generated by extracting important sentences for concise presentation.3.3.Answer ExtractionWe have implemented a general answer extraction module. For each named entity(NE)in the retrieved document that matches the question type(who,when,...),a score is calcu-lated using following features.•Degree of similarity between the user utterance and thedocument(3.2)•Number of matched content words in the sentence in-cluding the NE•Number of matched content words included in theclause that depends on/depended by the clause thatincludes the NEThe system then selects the NE with the highest score as an answer to the question.4.SYSTEM-INITIATIVE RECOMMENDATION For interactive information recommendation,we propose to generate system-initiative questions.They are semi-automatically made from the current document using theQA technique.This is complemented by conventional infor-mation recommendation techniques based on the document structure and document similarity.4.1.Generation of System-Initiative Questions(Method1)This method is intended to successively present more detailsof the target topic,after the initial summary presentation. The user may be interested in the part that was not includedin the summary.Although it is possible to prompt such as”Would you like more details?”,we propose a more interac-tive method by generating system-initiative questions in orderto attract interest of the user.A set of possible questions is prepared using the following procedure.It is almost reverse to the process toſnd an answerto the user’s question.1.Pick up the NE which may attract user’s interest basedon tf∗id f criterion.2.Substitute the NE with the corresponding interrogative.3.Delete the subordinate clause using a syntactic parser.4.Transform the sentence into interrogative formFigure3shows an example of transforming a sentence inthe KB into a question using the above mentioned procedure.Original:By the way,Queen Elizabeth praised this stonegarden very much,when...⇓(Substitute target NE into the corresponding interrogative) -By the way,who praised this stonegarden very much,when...⇓(Delete subordinate clause)-Who praised this stone garden very much?⇓(Transform into interrogative)Question:Do you know who praised this stone gardenvery much?Fig.3.Example of system-initiative quetion generation4.2.Recommendation based on Document Structure and SimilarityWe have also implemented two conventional recommenda-tion techniques based on the document structure and docu-ment similarity.•Recommendation based on document structure(Method2)Wikipedia documents are described hierarchically us-ing section structure.Thus,another section of the cur-rent document can be picked up for presentation.U1:Please explain Golden Pavilion.S1:Golden Pavilion is one of the buildings in the Rokuon-ji in Kyoto,and is the main attraction of the temple sites.The entire pavilion except the basementƀoor is coveredwith pure gold leaf.U2:When was it built?S2:Golden Pavilion was originally built in1397to serve asa retirement villa for Shogun Ashikaga Yoshimitsu. (Silence)S3:Well then,do you know what was awarded to this tem-ple in1994?U3:No,please tell me.S4:It was awarded as listing on the UNESCO World Her-itage in1994.U4:How can I get there?......Fig.4.Example dialogue •Recommendation based on document similarity (Method3)We can select a document that has a large similarity with the current document.This technique is adopted in information recommendation of Web pages.5.SYSTEM EV ALUATIONWe implemented a guidance system“Dialog Navigator for Kyoto City”.An example dialogue of the system using the QA technique is shown in Fig.4.We carried out aſeld trial at our university ers are in a wide variety of ages from children to senior people and apparently have few ex-periences in using spoken dialogue systems.No instructions on the system were given.In total2,500dialogue sessions (20,000utterances)were collected.In this paper,we eval-uated using427dialogue sessions chosen from a particular time period.For the ASR system,a trigram language model was trained using the KB,a dialogue corpus of different do-main,and Web texts[10].The average word accuracy was 70.6%.5.1.Evaluation in Question-Answering Performance First,we evaluated the performance of QA in terms of suc-cess rate3using366questions.We regarded QA as successful when the system made an appropriate response to the ques-tion.That is,if an answer to the question exists in the KB,we regarded QA as successful when the system presented the an-swer.On the other hands,if there is no answer in the KB,we regarded QA as successful when the system told it.The QA success rate was60.7%(62.9%in the case correct answers exist in the KB,and47.2%in the case when they do not).We also evaluated the effect of ASR conſdence measure (CM)for QA performance.The system used the CM as a 3Though the QA performance is usually evaluated using mean reciprocal rank(MRR),we adopt the simple success rate,because it is not possible to present alternative candidates via speech.Table2.Effect of using ASR conſdence measureUse of CM Success rate(%)Yes60.7(62.9,47.2)No55.7(54.0,66.0)Table3.Effect of using N-best hypothesesUse of N-best hypotheses Success rate(%) Merge3-best hypotheses(proposed)60.7(62.9,47.2) 1-best only(baseline)57.9(61.0,39.6) Optimal hypothesis(reference)63.1(65.8,47.2)Table4.Contextual effect for QAUse of context Success rate(%)Current topic(proposed)60.7(62.9,47.2)No context36.9(30.4,75.5)Previous one utterance54.6(54.3,56.6)All utterances55.5(56.5,49.1) weight in the matching between user query and the document in the KB.We compared with the case where the CM was not used.Table2lists these results,and conſrms the effect of the CM.Next,we evaluated the effect of using N-best hypotheses of the ASR result.In our system3-best hypotheses of the ASR result were used for making a query and extracting an answer.We compared with the case where only theſrst hy-pothesis was used(baseline).We also investigated the case where an optimal hypothesis was selected manually(refer-ence).Table3lists these results.The effect of using3-best hypotheses is clearly conſrmed,compared with the case to using only theſrst hypothesis.However,it was shown that higher success rate could be obtained if an optimal hypothe-sis was selected.This success rate could be achieved by in-troducing the conſrmation strategy[3].We then evaluated the effect of the context length(=num-ber of previous utterances)used for the retrieval.This result is shown in Table4.Without context,the success rate is signiſ-cantly degraded.But using all previous utterances has adverse effect.It was shown that incorporation of appropriate context information by topic tracking effectively improved the perfor-mance.5.2.Evaluation of System-Initiative RecommendationIn order to conſrm the effect of the proposed system-initiative question,the system was set to make possible recommenda-tions randomly.The number of recommendations presented by the system during the427dialogue sessions was319in to-tal.We regarded a recommendation as accepted when the user positively responded4to the proposal given by the system. The acceptance rate of each presentation technique is shown in Table5.The acceptance rate by the system-initiative ques-tion(method1)is much higher than that of other methods.4by human judgmentparison of recommendation methodRecommendation method Acceptance rate(%)Question(Proposed method1)74.7Document structure(Method2)51.1Document similarity(Method3)30.8The result suggests that recommendations using the question form are more interactive and attractive.6.CONCLUSIONSWe have proposed an interactive scheme for information guidance using question-answering techniques.In order to make interactive guidance,we incorporated question-answering techniques into both user-initiative information re-trieval and system-initiative information presentation.We have implemented a sightseeing guidance system and eval-uated with respect to QA-related techniques.It was shown that the QA-based technique worked well in improving the system performance.7.REFERENCES[1]K.Komatani,T.Kawahara,R.Ito,and H.G.Okuno,“Efſcientdialogue strategy toſnd users’intended items from informa-tion query results,”in Proc.COLING,2002,pp.481–487. 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