语音识别英文版介绍

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微软TTS语音引擎(speech api sapi)深度开发入门

Windows TTS开发介绍开篇介绍：我们都使用过一些某某词霸的英语学习工具软件，它们大多都有朗读的功能，其实这就是利用的Windows的TTS（Text To Speech)语音引擎。

它包含在Windows Speech SDK开发包中。

我们也可以使用此开发包根据自己的需要开发程序。

鸡啄米下面对TTS功能的软件开发过程进行详细介绍。

一.SAPI SDK的介绍SAPI，全称是The Microsoft Speech API。

就是微软的语音API。

由Windows Speech SDK提供。

Windows Speech SDK包含语音识别SR引擎和语音合成SS引擎两种语音引擎。

语音识别引擎用于识别语音命令，调用接口完成某个功能，实现语音控制。

语音合成引擎用于将文字转换成语音输出。

SAPI包括以下几类接口：Voice Commands API、Voice Dictation API、Voice Text API、Voice Telephone API和Audio Objects API。

我们要实现语音合成需要的是Voice Text API。

目前最常用的Windows Speech SDK版本有三种：5.1、5.3和5.4。

Windows Speech SDK 5.1版本支持xp系统和server 2003系统，需要下载安装。

XP系统默认只带了个Microsoft Sam英文男声语音库，想要中文引擎就需要安装Windows Speech SDK 5.1。

Windows Speech SDK 5.3版本支持Vista系统和Server 2008系统，已经集成到系统里。

Vista和Server 2003默认带Microsoft lili中文女声语音库和Microsoft Anna英文女声语音库。

Windows Speech SDK 5.4版本支持Windows7系统，也已经集成到系统里，不需要下载安装。

WEGASUN-M6语音识别模块产品使用说明

WEGASUN-M6语音识别模块产品使用说明书V2.0重要声明：本说明书仅用于WEGASUN-M6语音交互模块的入门辅助，对手册中的功能描述不做确定性保证，手册内容如有变动，恕不另行通知，可通过公司官网下载最新版本。

如模块功能性变动带来的损失本公司不承担任何责任。

未得到本公司书面许可，禁止引用本公司图案商标及文字商标。

目录产品介绍篇一.概述 (3)二.应用领域 (3)三.产品功能介绍 (3)四.产品性能参数 (4)五.产品及配件 (4)产品配置篇一.产品接线方法（以高配版为例） (5)二.安装USB设置驱动 (5)三.WEGASUN-M6语音识别专家软件介绍 (6)案例快速上手篇案例1，设置“识别词条”和“反馈语文本” (7)案例2，将“大管家”模式设置成“对话模式” (9)案例3，将对话模式设置成自定义唤醒模式 (10)案例4，将“反馈语音文本”设置为调用TF卡中的语音文件 (12)案例5，设置“词条缓冲区”，应用更多词条 (15)案例6，采用“调用记事本设置”一次性对多个指令进行设置 (16)案例7，更改“发音人”、“语速”、“音量”、“音调”等，实现不同播放效果 (19)智能控制设备篇一.语音控制智能插座 (20)二.语音控制智能开关 (24)三.语音控制四路继电器 (28)四.语音控制力沃墙壁开关 (31)五.语音控制杜亚窗帘机 (35)六.语音控制红外设备（未整理） (35)附件一．WEGASUN-M6核心板引脚定义 (39)二．最小系统框图 (40)三．WEGASUN-M6标准版模块电路理图 (40)一．概述WEGASUN-M6模块是珠海时代电子科技有限公司推出的一款集语音识别、语音合成、语音（MP3）点播、RF （射频）功能、红外功能于一体的多功能模块。

二．应用领域目前主要应用在智能家居、对话机器人、车载调度终端、高端智能语音交互玩具、楼宇智能化、教育机器人等方面。

语音识别中英文对照外文翻译文献

中英文资料对照外文翻译(文档含英文原文和中文翻译)Speech Recognition1 Defining the ProblemSpeech recognition is the process of converting an acoustic signal, captured by a microphone or a telephone, to a set of words. The recognized words can be the final results, as for applications such as commands & control, data entry, and document preparation. They can also serve as the input to further linguistic processing in order to achieve speech understanding, a subject covered in section.Speech recognition systems can be characterized by many parameters, some of the more important of which are shown in Figure. An isolated-word speech recognition system requires that the speaker pause briefly between words, whereas a continuous speech recognition system does not. Spontaneous, or extemporaneously generated, speech contains disfluencies, and is much more difficult to recognize than speech read from script. Some systems require speaker enrollment---a user must provide samples of his or her speech before using them, whereas other systems are said to be speaker-independent, in that no enrollment is necessary. Some of the other parameters depend on the specific task. Recognition is generally more difficult when vocabularies are large or have many similar-sounding words. When speech is produced in a sequence of words, language models or artificial grammars are used to restrict the combination of words.The simplest language model can be specified as a finite-state network, where the1permissible words following each word are given explicitly. More general language models approximating natural language are specified in terms of a context-sensitive grammar.One popular measure of the difficulty of the task, combining the vocabulary size and the language model, is perplexity, loosely defined as the geometric mean of the number of words that can follow a word after the language model has been applied (see section for a discussion of language modeling in general and perplexity in particular). Finally, there are some external parameters that can affect speech recognition system performance, including the characteristics of the environmental noise and the type and the placement of the microphone.Table: Typical parameters used to characterize the capability of speech recognition systems Speech recognition is a difficult problem, largely because of the many sources of variability associated with the signal. First, the acoustic realizations of phonemes, the smallest sound units of which words are composed, are highly dependent on the context in which they appear. These phonetic variabilities are exemplified by the acoustic differences of the phoneme，At word boundaries, contextual variations can be quite dramatic---making gas shortage sound like gash shortage in American English, and devo andare sound like devandare in Italian.Second, acoustic variabilities can result from changes in the environment as well as in the position and characteristics of the transducer. Third, within-speaker variabilities can result from changes in the speaker's physical and emotional state, speaking rate, or voice quality. Finally, differences in sociolinguistic background, dialect, and vocal tract size and shape can contribute to across-speaker variabilities.Figure shows the major components of a typical speech recognition system. The digitized speech signal is first transformed into a set of useful measurements or features at a fixed rate, typically once every 10--20 msec (see sectionsand 11.3 for signal representation and digital signal processing, respectively). These measurements are then used to search for the most likely word candidate, making use of constraints imposed by the acoustic, lexical, and language models. Throughout this process, training data are used to determine the values of the model parameters.Figure: Components of a typical speech recognition system.Speech recognition systems attempt to model the sources of variability described above in several ways. At the level of signal representation, researchers have developed representations that emphasize perceptually important speaker-independent features of the signal, and de-emphasize speaker-dependent characteristics. At the acoustic phonetic level, speaker variability is typically modeled using statistical techniques applied to large amounts of data. Speaker adaptation algorithms have also been developed that adapt speaker-independent acoustic models to those of the current speaker during system use, (see section). Effects of linguistic context at the acoustic phonetic level are typically handled by training separate models for phonemes in different contexts; this is called context dependent acoustic modeling.Word level variability can be handled by allowing alternate pronunciations of words in representations known as pronunciation networks. Common alternate pronunciations of words, as well as effects of dialect and accent are handled by allowing search algorithms to find alternate paths of phonemes through these networks. Statistical language models, based on estimates of the frequency of occurrence of word sequences, are often used to guide the searchthrough the most probable sequence of words.The dominant recognition paradigm in the past fifteen years is known as hidden Markov models (HMM). An HMM is a doubly stochastic model, in which the generation of the underlying phoneme string and the frame-by-frame, surface acoustic realizations are both represented probabilistically as Markov processes, as discussed in sections,and 11.2. Neural networks have also been used to estimate the frame based scores; these scores are then integrated into HMM-based system architectures, in what has come to be known as hybrid systems, as described in section 11.5.An interesting feature of frame-based HMM systems is that speech segments are identified during the search process, rather than explicitly. An alternate approach is to first identify speech segments, then classify the segments and use the segment scores to recognize words. This approach has produced competitive recognition performance in several tasks.2 State of the ArtComments about the state-of-the-art need to be made in the context of specific applications which reflect the constraints on the task. Moreover, different technologies are sometimes appropriate for different tasks. For example, when the vocabulary is small, the entire word can be modeled as a single unit. Such an approach is not practical for large vocabularies, where word models must be built up from subword units.Performance of speech recognition systems is typically described in terms of word error rate E, defined as:where N is the total number of words in the test set, and S, I, and D are the total number of substitutions, insertions, and deletions, respectively.The past decade has witnessed significant progress in speech recognition technology. Word error rates continue to drop by a factor of 2 every two years. Substantial progress has been made in the basic technology, leading to the lowering of barriers to speaker independence, continuous speech, and large vocabularies. There are several factors that have contributed to this rapid progress. First, there is the coming of age of the HMM. HMM is powerful in that, with the availability of training data, the parameters of the model can be trained automatically to giveoptimal performance.Second, much effort has gone into the development of large speech corpora for system development, training, and testing. Some of these corpora are designed for acoustic phonetic research, while others are highly task specific. Nowadays, it is not uncommon to have tens of thousands of sentences available for system training and testing. These corpora permit researchers to quantify the acoustic cues important for phonetic contrasts and to determine parameters of the recognizers in a statistically meaningful way. While many of these corpora (e.g., TIMIT, RM, ATIS, and WSJ; see section 12.3) were originally collected under the sponsorship of the U.S. Defense Advanced Research Projects Agency (ARPA) to spur human language technology development among its contractors, they have nevertheless gained world-wide acceptance (e.g., in Canada, France, Germany, Japan, and the U.K.) as standards on which to evaluate speech recognition.Third, progress has been brought about by the establishment of standards for performance evaluation. Only a decade ago, researchers trained and tested their systems using locally collected data, and had not been very careful in delineating training and testing sets. As a result, it was very difficult to compare performance across systems, and a system's performance typically degraded when it was presented with previously unseen data. The recent availability of a large body of data in the public domain, coupled with the specification of evaluation standards, has resulted in uniform documentation of test results, thus contributing to greater reliability in monitoring progress (corpus development activities and evaluation methodologies are summarized in chapters 12 and 13 respectively).Finally, advances in computer technology have also indirectly influenced our progress. The availability of fast computers with inexpensive mass storage capabilities has enabled researchers to run many large scale experiments in a short amount of time. This means that the elapsed time between an idea and its implementation and evaluation is greatly reduced. In fact, speech recognition systems with reasonable performance can now run in real time using high-end workstations without additional hardware---a feat unimaginable only a few years ago.One of the most popular, and potentially most useful tasks with low perplexity (PP=11) is the recognition of digits. For American English, speaker-independent recognition of digit strings spoken continuously and restricted to telephone bandwidth can achieve an error rate of 0.3% when the string length is known.One of the best known moderate-perplexity tasks is the 1,000-word so-called Resource Management (RM) task, in which inquiries can be made concerning various naval vessels in the Pacific ocean. The best speaker-independent performance on the RM task is less than 4%, using a word-pair language model that constrains the possible words following a given word (PP=60). More recently, researchers have begun to address the issue of recognizing spontaneously generated speech. For example, in the Air Travel Information Service (ATIS) domain, word error rates of less than 3% has been reported for a vocabulary of nearly 2,000 words and a bigram language model with a perplexity of around 15.High perplexity tasks with a vocabulary of thousands of words are intended primarily for the dictation application. After working on isolated-word, speaker-dependent systems for many years, the community has since 1992 moved towards very-large-vocabulary (20,000 words and more), high-perplexity (PP≈200), speaker-independent, continuous speech recognition. The best system in 1994 achieved an error rate of 7.2% on read sentences drawn from North America business news.With the steady improvements in speech recognition performance, systems are now being deployed within telephone and cellular networks in many countries. Within the next few years, speech recognition will be pervasive in telephone networks around the world. There are tremendous forces driving the development of the technology; in many countries, touch tone penetration is low, and voice is the only option for controlling automated services. In voice dialing, for example, users can dial 10--20 telephone numbers by voice (e.g., call home) after having enrolled their voices by saying the words associated with telephone numbers. AT&T, on the other hand, has installed a call routing system using speaker-independent word-spotting technology that can detect a few key phrases (e.g., person to person, calling card) in sentences such as: I want to charge it to my calling card.At present, several very large vocabulary dictation systems are available for document generation. These systems generally require speakers to pause between words. Their performance can be further enhanced if one can apply constraints of the specific domain such as dictating medical reports.Even though much progress is being made, machines are a long way from recognizing conversational speech. Word recognition rates on telephone conversations in the Switchboard corpus are around 50%. It will be many years before unlimited vocabulary, speaker-independentcontinuous dictation capability is realized.3 Future DirectionsIn 1992, the U.S. National Science Foundation sponsored a workshop to identify the key research challenges in the area of human language technology, and the infrastructure needed to support the work. The key research challenges are summarized in. Research in the following areas for speech recognition were identified:Robustness:In a robust system, performance degrades gracefully (rather than catastrophically) as conditions become more different from those under which it was trained. Differences in channel characteristics and acoustic environment should receive particular attention.Portability:Portability refers to the goal of rapidly designing, developing and deploying systems for new applications. At present, systems tend to suffer significant degradation when moved to a new task. In order to return to peak performance, they must be trained on examples specific to the new task, which is time consuming and expensive.Adaptation:How can systems continuously adapt to changing conditions (new speakers, microphone, task, etc) and improve through use? Such adaptation can occur at many levels in systems, subword models, word pronunciations, language models, etc.Language Modeling:Current systems use statistical language models to help reduce the search space and resolve acoustic ambiguity. As vocabulary size grows and other constraints are relaxed to create more habitable systems, it will be increasingly important to get as much constraint as possible from language models; perhaps incorporating syntactic and semantic constraints that cannot be captured by purely statistical models.Confidence Measures:Most speech recognition systems assign scores to hypotheses for the purpose of rank ordering them. These scores do not provide a good indication of whether a hypothesis is correct or not, just that it is better than the other hypotheses. As we move to tasks that require actions,we need better methods to evaluate the absolute correctness of hypotheses.Out-of-Vocabulary Words:Systems are designed for use with a particular set of words, but system users may not know exactly which words are in the system vocabulary. This leads to a certain percentage of out-of-vocabulary words in natural conditions. Systems must have some method of detecting such out-of-vocabulary words, or they will end up mapping a word from the vocabulary onto the unknown word, causing an error.Spontaneous Speech:Systems that are deployed for real use must deal with a variety of spontaneous speech phenomena, such as filled pauses, false starts, hesitations, ungrammatical constructions and other common behaviors not found in read speech. Development on the ATIS task has resulted in progress in this area, but much work remains to be done.Prosody:Prosody refers to acoustic structure that extends over several segments or words. Stress, intonation, and rhythm convey important information for word recognition and the user's intentions (e.g., sarcasm, anger). Current systems do not capture prosodic structure. How to integrate prosodic information into the recognition architecture is a critical question that has not yet been answered.Modeling Dynamics:Systems assume a sequence of input frames which are treated as if they were independent. But it is known that perceptual cues for words and phonemes require the integration of features that reflect the movements of the articulators, which are dynamic in nature. How to model dynamics and incorporate this information into recognition systems is an unsolved problem.语音识别一定义问题语音识别是指音频信号的转换过程，被电话或麦克风的所捕获的一系列的消息。

机器人语音识别中英文对照外文翻译文献

中英文资料外文翻译译文:改进型智能机器人的语音识别方法2、语音识别概述最近，由于其重大的理论意义和实用价值，语音识别已经受到越来越多的关注。

到现在为止，多数的语音识别是基于传统的线性系统理论，例如隐马尔可夫模型和动态时间规整技术。

随着语音识别的深度研究，研究者发现，语音信号是一个复杂的非线性过程，如果语音识别研究想要获得突破，那么就必须引进非线性系统理论方法。

最近，随着非线性系统理论的发展，如人工神经网络，混沌与分形，可能应用这些理论到语音识别中。

因此，本文的研究是在神经网络和混沌与分形理论的基础上介绍了语音识别的过程。

语音识别可以划分为独立发声式和非独立发声式两种。

非独立发声式是指发音模式是由单个人来进行训练，其对训练人命令的识别速度很快，但它对与其他人的指令识别速度很慢，或者不能识别。

独立发声式是指其发音模式是由不同年龄，不同性别，不同地域的人来进行训练，它能识别一个群体的指令。

一般地，由于用户不需要操作训练，独立发声式系统得到了更广泛的应用。

所以，在独立发声式系统中，从语音信号中提取语音特征是语音识别系统的一个基本问题。

语音识别包括训练和识别，我们可以把它看做一种模式化的识别任务。

通常地，语音信号可以看作为一段通过隐马尔可夫模型来表征的时间序列。

通过这些特征提取，语音信号被转化为特征向量并把它作为一种意见，在训练程序中，这些意见将反馈到HMM的模型参数估计中。

这些参数包括意见和他们响应状态所对应的概率密度函数，状态间的转移概率，等等。

经过参数估计以后，这个已训练模式就可以应用到识别任务当中。

输入信号将会被确认为造成词，其精确度是可以评估的。

整个过程如图一所示。

图1 语音识别系统的模块图3、理论与方法从语音信号中进行独立扬声器的特征提取是语音识别系统中的一个基本问题。

解决这个问题的最流行方法是应用线性预测倒谱系数和Mel频率倒谱系数。

这两种方法都是基于一种假设的线形程序，该假设认为说话者所拥有的语音特性是由于声道共振造成的。

nuance

声纹鉴别技术
在以ASR技术为基础的情况下，Nuance公司又实现了声纹鉴别技术，该技术属于“生物因子”认证范畴。同指纹一样，声纹同样是不可复制的，每个人的指纹都是唯一的，数百万人之间才会发现有两个人有相同的指纹；与此类似，声纹也是人的个性特征，很难找到两个声纹完全一样的人。说话人识别，也称声纹鉴别，就是根据人的声音特征，鉴别出某段语音是谁说的。
国内情况
08年3月，亿讯成为大中华区的专业总代理。在中国有90%的语音识别应用是采用Nuance的核心技术。在中国，占据大部分客服呼叫中心的份额，尤其在电信、金融行业广泛应用。和电信、移动、联通、网通都有合作，cctv春晚的呼叫中心也应用此技术。
重点关注产品
桌面产品包括Dragon NatuallySpeaking 10，PDF Converter Professional 5，OmniPage 16，PaperPort 11。
Nuance公司（Nuance Communications, Inc. (NASDAQ: NUAN)) 是最大的专门从事语音识别软件、图像处理软件及输入法软件研发、销售的公司。目前世界上最先进的电脑语音识别软件Naturally Speaking就出自于Nuance公司。用户对着麦克风说话，屏幕上就会显示出说话的内容。T9智能文字输入法作为旗舰产品，最大优势支持超过70种语言，超过30亿部移动设备内置T9输入法。已成为业内认同的标准输入法，被众多OEM厂商内置，包括诺基亚、索爱、三星、LG、夏普、海尔、华为等等。T9全球市场占有率超70%，中国超50%。公司logo自电脑问世以来，科学家们就一直致力于让电脑能够理解人们的讲话。几年前，除了实验室内的演示之外，这方面还没有什么进展。不过现在电脑的语音识别功能已经有了质的飞跃，随着语音识别技术慢慢走向成熟，驾驶员可以“告诉”全球定位系统（GPS）他们的目的地；手机用户不必按键，只需要对着手机发布命令即可；医生可以口述患者的病历，而旁边的设备就能自动记录下来,这一切通过口头指令来控制操作的应用现在已经不仅仅出现在科幻小说中了，而是真正成为了现实。

WEGASUN-M6语音识别模块产品使用说明

如模块功能性变动带来的损失本公司不承担任何责任。

未得到本公司书面许可，禁止引用本公司图案商标及文字商标。

二．应用领域目前主要应用在智能家居、对话机器人、车载调度终端、高端智能语音交互玩具、楼宇智能化、教育机器人等方面。

iPhone Siri语音识别软件

语言模式识别系统是对用户输入的表层、语法层、习惯
从纯技术角度，Siri 是一套较准确的语音识别系统，也是具有环境感知能力的持续自学习系统，更是一整套集成上述所有优点的新型人工智能框架。本质上，Siri 是通过多轮对话来获得用户事务意图的意图识别系统。为了做到这一点，其内部综合了语音识别、自然语言理解、任务控制、意图理解、服务集成等多项技术。用一句话来概述 Siri：Siri 能理解用户的语言语义，并经过多轮会话弄清用户的意图后，集成能够满足用户意图的多种服务（内部和外部）来帮助用户达成特定领域的事务。
接口，其针对用户询问所给予的回答，也不至于答非所问，有时候更是
让人有种心有灵犀的惊喜，例如使用者如果在说出、输入的内容包括了『喝了点』、『家』这些字（甚至不需要符合语法，相当人性化...）， Siri 则会判断为喝醉酒、要回家，并自动建议是否要帮忙叫出租车。
十一大功能
1.Siri 变身闹钟 2.用 Siri 寻找咖啡厅 3.想去哪，Siri 告诉你 4.用 Siri 播放随机音乐 5.发送短信，Siri 代劳 6.天气预报，Siri 知道 7.用Siri提醒日程安排 8.用 Siri 提醒地点 9.Siri 为你答疑解惑 10.用 Siri 发送微博（支持新浪微博）
发展预测
Siri 在技术上的贡献在于，它为我们设定了新的目标，即“基于实际生活的自然语言理解”（practical natural language understanding）。尽管用户通过语音而不是键盘沟通这个构想由来已久，不过整个产业花了超过三十年来实现，机器通过语音识别跟用户间的无障碍自然沟通。开发基于有限的词汇和语音识别功能的软件是第一步，实际上很多电话呼叫中心已经实现了这一点。

5语音识别技术(2)

语音识别技术语音识别技术，也被称为自动语音识别Automatic Speech Recognition，(ASR)，其目标是将人类的语音中的词汇内容转换为计算机可读的输入，例如按键、二进制编码或者字符序列。

与说话人识别及说话人确认不同，后者尝试识别或确认发出语音的说话人而非其中所包含的词汇内容。

1.语音识别技术定义语音识别技术，也被称为自动语音识别AutomaTIc Speech RecogniTIon，（ASR），其目标是将人类的语音中的词汇内容转换为计算机可读的输入，例如按键、二进制编码或者字符序列。

与说话人识别及说话人确认不同，后者尝试识别或确认发出语音的说话人而非其中所包含的词汇内容。

语音识别技术的应用包括语音拨号、语音导航、室内设备控制、语音文档检索、简单的听写数据录入等。

语音识别技术与其他自然语言处理技术如机器翻译及语音合成技术相结合，可以构建出更加复杂的应用，例如语音到语音的翻译。

2.语音识别技术原理声音实际上是一种波。

常见的mp3、wmv等格式都是压缩格式，必须转成非压缩的纯波形文件来处理，比如Windows PCM文件，也就是俗称的wav文件。

wav文件里存储的除了一个文件头以外，就是声音波形的一个个点了。

下图是一个波形的示例。

在开始语音识别之前，有时需要把首尾端的静音切除，降低对后续步骤造成的干扰。

这个静音切除的操作一般称为VAD，需要用到信号处理的一些技术。

要对声音进行分析，需要对声音分帧，也就是把声音切开成一小段一小段，每小段称为一帧。

分帧操作一般不是简单的切开，而是使用移动窗函数来实现，这里不详述。

帧与帧之间一般是有交叠的，就像下图这样：图中，每帧的长度为25毫秒，每两帧之间有25-10=15毫秒的交叠。

我们称为以帧长25ms、帧移10ms分帧。

图中，每帧的长度为25毫秒，每两帧之间有25-10=15毫秒的交叠。

我们称为以帧长25ms、帧移10ms分帧。

分帧后，语音就变成了很多小段。

《语音识别技术介绍》课件

智能家居安全
通过语音识别技术，可以实时监测家庭环境，及时发现异常情况并发出警报，提高家庭安全系数。
智能家居助手
语音识别技术可以应用于智能家居助手，提供天气预报、日程提醒、语音记事等服务，方便用户日常生活。
在医疗领域的应用前景
语音电子病历
通过语音识别技术，医生可以快速录入病历信息，提高工作效率，减少医疗差错。
01
语音识别技术面临的挑战
环境噪音与口音差异
环境噪音
在现实生活中，语音识别技术常常面临着各种环境噪音的干扰，如汽车轰鸣声、人群喧闹声等。这些噪音可能会影响语音识别的准确性，使技术难以分辨出清晰、准确的语音信号。
口音差异
不同地区、不同人群的口音和语言习惯可能存在较大差异，这给语音识别技术带来了挑战。例如，方言、俚语、口音等都可能影响语音识别的准确性。
语音识别技术介绍
THE FIRST LESSON OF THE SCHOOL YEAR
目录CONTENTS
• 语音识别技术概述 • 语音识别技术原理 • 语音识别技术面临的挑战 • 语音识别技术的发展趋势 • 语音识别技术的前景展望 • 语音识别技术案例分析
01
语音识别技术概述
定义与特点
定义
语音识别技术是一种将人类语音转化为机器可读的文本或命令的技术。
随着传感器技术的发展和人工智能算法的进步，多模态语音识别与交互将成为未来语音识别技术的重要发展方向。通过结合不同模态的信息，能够提高语音识别的性能，并为用户提供更加智能和自然的交互体验。
01
语音识别技术的前景展望
在智能家居领域的应用前景
1 2 3
智能音箱控制
语音识别技术可以应用于智能音箱，实现通过语音指令控制家电设备，如灯光、空调、电视等。

语音识别引擎介绍

语音识别引擎介绍1.语音识别技术简介语音识别技术，也被称为自动语音识别Automatic Speech Recognition，(ASR)，其目标是将人类的语音中的词汇内容转换为计算机可读的输入，例如按键、二进制编码或者字符序列。

与说话人识别及说话人确认不同，后者尝试识别或确认发出语音的说话人而非其中所包含的词汇内容。

语音识别技术的应用包括语音拨号、语音导航、室内设备控制、语音文档检索、简单的听写数据录入等。

语音识别技术与其他自然语言处理技术如机器翻译及语音合成技术相结合，可以构建出更加复杂的应用，例如语音到语音的翻译。

语音识别技术所涉及的领域包括：信号处理、模式识别、概率论和信息论、发声机理和听觉机理、人工智能等等。

2.语音识别技术详解目前，主流的大词汇量语音识别系统多采用统计模式识别技术。

典型的基于统计模式识别方法的语音识别系统由以下几个基本模块所构成信号处理及特征提取模块。

该模块的主要任务是从输入信号中提取特征，供声学模型处理。

同时，它一般也包括了一些信号处理技术，以尽可能降低环境噪声、信道、说话人等因素对特征造成的影响。

统计声学模型。

典型系统多采用基于一阶隐马尔科夫模型进行建模。

发音词典。

发音词典包含系统所能处理的词汇集及其发音。

发音词典实际提供了声学模型建模单元与语言模型建模单元间的映射。

语言模型。

语言模型对系统所针对的语言进行建模。

理论上，包括正则语言，上下文无关文法在内的各种语言模型都可以作为语言模型，但目前各种系统普遍采用的还是基于统计的N元文法及其变体。

解码器。

解码器是语音识别系统的核心之一，其任务是对输入的信号，根据声学、语言模型及词典，寻找能够以最大概率输出该信号的词串。

从数学角度可以更加清楚的了解上述模块之间的关系。

首先，统计语音识别的最基本问题是，给定输入信号或特征序列，符号集（词典），求解符号串使得：W = argmaxP(W | O) 通过贝叶斯公式，上式可以改写为由于对于确定的输入串O，P(O)是确定的，因此省略它并不会影响上式的最终结果，因此，一般来说语音识别所讨论的问题可以用下面的公式来表示，可以将它称为语音识别的基本公式。

语音识别技术概述

语音识别技术概述语音是人类最自然的交互方式。

计算机发明之后，让机器能够“听懂”人类的语言，理解语言中的内在含义，并能做出正确的回答就成为了人们追求的目标。

我们都希望像科幻电影中那些智能先进的机器人助手一样，在与人进行语音交流时，让它听明白你在说什么。

语音识别技术将人类这一曾经的梦想变成了现实。

语音识别就好比“机器的听觉系统”，该技术让机器通过识别和理解，把语音信号转变为相应的文本或命令。

语音识别技术，也被称为自动语音识别AutomaTIc Speech RecogniTIon，(ASR)，其目标是将人类的语音中的词汇内容转换为计算机可读的输入，例如按键、二进制编码或者字符序列。

语音识别就好比“机器的听觉系统”，它让机器通过识别和理解，把语音信号转变为相应的文本或命令。

语音识别是一门涉及面很广的交叉学科，它与声学、语音学、语言学、信息理论、模式识别理论以及神经生物学等学科都有非常密切的关系。

语音识别技术正逐步成为计算机信息处理技术中的关键技术。

语音识别技术的发展语音识别技术的研究最早开始于20世纪50年代，1952 年贝尔实验室研发出了 10 个孤立数字的识别系统。

从 20 世纪 60 年代开始，美国卡耐基梅隆大学的 Reddy 等开展了连续语音识别的研究，但是这段时间发展很缓慢。

1969年贝尔实验室的 Pierce J 甚至在一封公开信中将语音识别比作近几年不可能实现的事情。

20世纪80年代开始，以隐马尔可夫模型(hidden Markov model，HMM)方法为代表的基于统计模型方法逐渐在语音识别研究中占据了主导地位。

HMM模型能够很好地描述语音信号的短时平稳特性，并且将声学、语言学、句法等知识集成到统一框架中。

此后，HMM的研究和应用逐渐成为了主流。

例如，第一个“非特定人连续语音识别系统”是当时还在卡耐基梅隆大学读书的李开复研发的SPHINX系统，其核心框架就是GMM-HMM框架，其中GMM(Gaussian mixturemodel，高斯混合模型)用来对语音的观察概率进行建模，HMM则对语音的时序进行建模。

语音识别(speechrecognition)

差，找出最小的失真误差对应的码本（代表一个
字），将对应的字输出作为识别的结果。
码本每一个字做一个码本，共M个字
Y1 Y2 YM
模板库
任意语音帧
特征矢量 X 序列形成
计算输出结果Yi 失真误差判决
特征矢量序列模板库
X＝{X1 , X2 , …… , XN} Y1 , Y2 , …… , YM
语音识别（speech recognition）
语音识别技术的一般概念
语音识别的原理和识别系统的组成
动态时间规整DTW
基于统计模型框架的识别法(HMM)
说话人识别
语种辨识
语音识别技术的一般概念
一、语音识别的定义二、语音识别的应用
三、语音识别的类型
四、语音识别的方法
五、语音识别的主要问题
一、语音识别的定义
多领域。
随着语音识别技术的逐渐成熟，语音识别技术开
始得到广泛的应用，涉及日常生活的各个方面如电信、
金融、新闻、公共事业等各个行业，通过采用语音识
别技术，可以极大的简化这些领域的业务流程以及操
作；提高系统的应用效率。
语音识别应用实例
1.语音识别以IBM推出的ViaVoice为代表，国内
则推出Dutty ++语音识别系统、天信语音识别系统、
语音识别是指从语音到文本的转换，即让计算
机能够把人发出的有意义的话音变成书面语言。通
俗地说就是让机器能够听懂人说的话。
所谓听懂，有两层意思，一是指把用户所说的
话逐词逐句转换成文本；二是指正确理解语音中所
包含的要求，作出正确的应答。
二、语音识别的应用
语音识别技术是以语音为研究对象，涉及到生理学、心理学、语言学、计算机科学以及信号处理等诸

TMM使用介绍

Tell Me More之使用入门这篇文章是用来帮助刚着手用Tell Me More的朋友尽快地进入学习状态，而不用将时间浪费在摸索Tell Me More的功能上。

这种最初级的入门文章舍得不打算多写。

在这篇文章之后，舍得会重点介绍Tell Me More的语音训练方面的功能，因为那才是Tell Me More的核心。

安装的部分这里不多讲，舍得在《安装必读》一文章中已经简单的介绍过了，如果有朋友不懂安装的，请给舍得留言，舍得可以再写一篇详尽的教程，但不是现在。

使用Tell Me More的第一步，当然是建立一个Tell Me More的帐号啦，非常简单，点下图的“创建一个新帐户”，然后按照提示操作就可以：接下来是选择级别了吧，Performance版共10个级别，你可以根据自己的水平选择相应的级别，若想基础打扎实点，从1级开始学也未尝不可：然后要选择的就是模式，舍得推荐大家使用向导模式，在这种模式下，你可以让软件来监督你的学习，这也是用软件的一大好处，如果是纯粹拿书本自学的话，往往会找不到北，学着学着心就散了。

用软件来辅助学习就不一样，在向导模式中，你可以随时看到自己的进度，这个呆会再讲：选择了向导模式后，主页上显示的是这样子的：标黑色的小方块就是你已经完成的练习，在你坚持了一段时间之后，看到一大片练习已经变成了“黑色”，舍得相信，此时你更容易坚持下去，因为成功就在眼前啊！其次舍得要推荐的是“漫游模式”，这里你可以选择不同的训练模式，它是按照词汇、语法、口语等不同的版块来进行划分。

舍得在《口语训练初识》一文中的介绍就是基于这个“漫游模式”为什么没推荐动态模式呢？这个模式应该是7.0版之后才加进来的，舍得在使用9.0版的时候，总觉得用起来别扭，即没有漫游模式的高自由度，又没有向导模式的计划性。

不过萝卜青菜各有所爱，大家不要受舍得的“偏见”所限，自己去尝试一下，看看哪个模式最适合你。

本文中，舍得主要探讨的是向导模式，一理通百理通，掌握了向导模式之后，其它模式就不在话下了，因为训练的内容和方式基本上是一致的。

语音识别英文版介绍

Speech recognitionLouise WangLanguage teaching in computers and networks has become an effective aid to traditional language teaching, and speech recognition technology has become a relatively new technology in computer-aided language learning. However, the application of this technology in language learning and human-computer interaction oral practice is still in the exploration stage. Speech recognition technology is one of the ten important technology development technologies in the field of information technology from 2000 to 2010. It is becoming a key technology for human-computer interaction in information technology.In the speech recognition experience class, the teacher guides the students to assemble the "smart fish lamp", explain the corresponding graphical programming program, and guide the students to learn the concept and discriminative features of voiceprint recognition through software and hardware. The combination of speech recognition technology and speech synthesis technology allows people to operate with voice commands without the need for a keyboard. The application of voice technology has become a competitive emerging high-tech industry.The new curriculum method, the vivid analysis of speech recognition knowledge, software and hardware knowledge, programming knowledge,enables students to better understand the working principle of artificial in tellige nee speech recog niti on, and exercise the stude nts' ability to brain and hands and teamwork.The purpose of speech recog niti on is to con vert vocabulary content in human speech into vocabulary content contained in a computer. Speech recog niti on in cludes voice diali ng, voice n avigati on, in door device con trol, voice document retrieval, simple dictation data entry, and the ability to build more complex applicati ons.Speech recognition is a technique for solving the problem of "understanding" in human Ianguage. At present, the research on speech recog niti on tech no logy has made breakthroughs. Speech recog niti on tech no logies such as voice teleph one excha nge, i nformatio n n etwork inqu iry, home service, hotel service, medical service, banking service, in dustrial con trol, voice com muni catio n system, etc., almost in volve various lines. Every aspect of in dustry and society.Un veiled the mystery of speech recog niti on and closely linked artificial in tellige nee to the lear ning and life of stude nts. By experie ncing the speech recognition course, students can deeply understand the infinite mystery of speech recog niti on and the profo und impact on people's lives.nmlllil。

百度百科—语音识别

语音识别与机器进行语音交流，让机器明白你说什么，这是人们长期以来梦寐以求的事情。

语音识别技术就是让机器通过识别和理解过程把语音信号转变为相应的文本或命令的高技术。

语音识别技术主要包括特征提取技术、模式匹配准则及模型训练技术三个方面。

任务分类和应用根据识别的对象不同，语音识别任务大体可分为3类，即孤立词识别（isolated word recognition)，关键词识别（或称关键词检出，keyword spotting)和连续语音识别。

其中，孤立词识别的任务是识别事先已知的孤立的词，如“开机”、“关机”等；连续语音识别的任务则是识别任意的连续语音，如一个句子或一段话；连续语音流中的关键词检测针对的是连续语音，但它并不识别全部文字，而只是检测已知的若干关键词在何处出现，如在一段话中检测“计算机”、“世界”这两个词。

根据针对的发音人，可以把语音识别技术分为特定人语音识别和非特定人语音识别，前者只能识别一个或几个人的语音，而后者则可以被任何人使用。

显然，非特定人语音识别系统更符合实际需要，但它要比针对特定人的识别困难得多。

另外，根据语音设备和通道，可以分为桌面（PC）语音识别、电话语音识别和嵌入式设备（手机、PDA等）语音识别。

不同的采集通道会使人的发音的声学特性发生变形，因此需要构造各自的识别系统。

语音识别的应用领域非常广泛，常见的应用系统有：语音输入系统，相对于键盘输入方法，它更符合人的日常习惯，也更自然、更高效；语音控制系统，即用语音来控制设备的运行，相对于手动控制来说更加快捷、方便，可以用在诸如工业控制、语音拨号系统、智能家电、声控智能玩具等许多领域；智能对话查询系统，根据客户的语音进行操作，为用户提供自然、友好的数据库检索服务，例如家庭服务、宾馆服务、旅行社服务系统、订票系统、医疗服务、银行服务、股票查询服务等等。

语音识别方法语音识别方法主要是模式匹配法。

在训练阶段，用户将词汇表中的每一词依次说一遍，并且将其特征矢量作为模板存入模板库。

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