Speech Recognition, Machine Translation, and Speech

语音识别技术是什么_语音识别技术应用领域介绍语音识别技术，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。

语音识别系统提示客户在新的场合使用新的口令密码，这样使用者不需要记住固定的口令，系统也不会被录音欺骗。

文本相关的声音识别方法可以分为动态时间伸缩或隐马尔可夫模型方法。

文本无关声音识别已经被研究很长时间了，不一致环境造成的性能下降是应用中的一个很大的障碍。

其工作原理：动态时间伸缩方法使用瞬间的、变动倒频。

1963年Bogert et al出版了《回声的时序倒频分析》。

通过交换字母顺序，他们用一个含义广泛的词汇定义了一个新的信号处理技术，倒频谱的计算通常使用快速傅立叶变换。

从1975年起，隐马尔可夫模型变得很流行。

运用隐马尔可夫模型的方法，频谱特征的统计变差得以测量。

文本无关语音识别方法的例子有平均频谱法、矢量量化法和多变量自回归法。

平均频谱法使用有利的倒频距离，语音频谱中的音位影响被平均频谱去除。

使用矢量量化法，语者的一套短期训练的特征向量可以直接用来描绘语者的本质特征。

但是，当训练向量的数量很大时，这种直接的描绘是不切实际的，因为存储和计算的量变得离奇的大。

所以尝试用矢量量化法去寻找有效的方法来压缩训练数据。

Montacie et al在倒频向量的时序中应用多变量自回归模式来确定语者特征，取得了很好的效果。

想骗过语音识别系统要有高质量的录音机，那不是很容易买到的。

一般的录音机不能记录声音的完整频谱，录音系统的质量损失也必须是非常低的。

对于大多数的语音识别系统，模仿的声音都不会成功。

用语音识别来辨认身份是非常复杂的，所以语音识别系统会结合个人身份号码识别或芯片卡。

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。

人工智能中的自然语言处理与机器翻译随着科技的不断发展，人工智能（Artificial Intelligence，简称AI）已经成为一个不可忽视的领域。

其中，自然语言处理（Natural Language Processing，简称NLP）和机器翻译（Machine Translation，简称MT）更是备受瞩目。

本文将从理论与实践两个层面探讨人工智能中的自然语言处理与机器翻译。

一、自然语言处理自然语言处理是指让计算机理解人类自然语言的一种技术，可以分为语音识别、自然语言理解、自然语言生成等多个方面。

其中，语音识别是最基础的环节，它能将人类语音转化成计算机可以理解的数字信号。

自然语言理解则是在语音识别的基础上，计算机能够将语音转化成一些可供算法运算的文本，同时计算机还能够分析出文本中的词汇义项、句法结构等。

自然语言生成则是让计算机通过一些算法生成自然语言的过程。

自然语言处理有着广泛的应用场景，比如人机对话系统、智能问答系统、智能语音助手等等。

其中最为典型的应用就是智能语音助手，如Apple的Siri、Amazon的Alexa、Google的Assistant等。

这些技术的应用场景越来越广泛，相信未来还有更多的技术会来丰富这个领域。

二、机器翻译机器翻译最初的研究是在二战期间进行的，当时美国军方急需获得外国情报，但又缺乏翻译人员，于是便提出了机器翻译的概念。

随着计算机技术的不断发展，机器翻译的研究也得以不断完善。

机器翻译主要有基于规则的方法、统计机器翻译、神经机器翻译等多种方式。

目前，机器翻译的应用场景已经十分广泛，比如国际贸易及其相关服务、自然语言学习教育、新闻资讯报道等等。

举一个最直观的例子，像谷歌翻译这样的机器翻译引擎，它已经成为许多非英语国家用户的生活必备工具，能够让人们更快捷地获取各类资讯和信息。

机器翻译技术的发展得到了很好的应用，但是，英语以外的语言仍是机器翻译的一个难点。

特别是对于中文这样的语言，语序的复杂性和词汇表的庞大性极大增加了机器翻译的难度。

Chapter14Speech recognition Mikko Kurimo,Panu Somervuo,Vesa Siivola14514.1Acoustic modelingThe general goal of automatic speech recognition(ASR)is to understand normal human speech and then to be able to perform some task based on this understanding.One application of ASR is a dictation system which converts spoken sentences into their written forms.In this sense the speech recognition can be defined as the mapping from the continuous acoustic signal to the discrete set of symbols.There are several reasons for the difficulties in speech recognition.Natural speech has variations in many levels.In addition to that different speakers have different voices, there is also considerable variation in the voice of a single speaker.In normal conversation, some parts of the words may be emphasized more than others depending on the context. The loudness and the pitch of the voice may change and also the speaking rate may vary. Even if the speaker tries to use as steady a voice as possible,no two uttered sounds are generally equal.Therefore,in the speech recognition system,some limitations are usually made concerning the nature of the speech to be recognized.These limitations may include the number of the speakers,the size of the vocabulary,the amount of the noise in the speech,and the assumption that the input will always be speech.Our projects in automatic speech recognition are aimed both to use the recognition system as a test bench for the neural network algorithms developed in the laboratory and to develop the system itself as a pilot application of the neural networks.Besides developing new recognition algorithms,we have also investigated new acoustic features and context modeling.Examples of the applications where we have used SOM and LVQ algorithms are shown in Table14.1and Figure14.1.S1S2S5S9Figure14.1:Competitive-learning segment models on the SOM.Each map node is asso-ciated with an HMM(with three states in this case)instead of a traditionally used single feature vector.The thick line represents the Viterbi segmentation of one input sequence. This corresponds to the best matching unit(BMU)search.The models of the BMUs and neighboring units are then updated by the corresponding segments.The block diagram of a speech recognition system is shown in Figure14.2.The recog-nition is based on connecting the hidden Markov models(HMMs)of the phonemes to decode the phoneme sequences of the spoken utterances.The output density function of each state in each model is a mixture of multivariate Gaussian densities.We have used the following scheme for the training of the models[1].SOM is usedfirst for initializ-ing the phoneme-wise codebooks.Each model vector becomes then a mean vector of aGaussian kernel.After initialization,the training is continued by segmental-SOM or K-means algorithm.Segmental-LVQ is then applied for error-corrective training in order to obtain better phoneme discrimination.In the context of mixture density HMMs,we have developed methods for speeding up the recognition[1,3]based on the SOM structure.Figure14.2:Overview of an ASR system.Model associated with the SOM node:Application:1.feature vector kernel means of mixture densities[1]2.feature vector sequence variable-length word templates[3]3.hidden Markov model set of(non-linguistic)speech segments[3]4.symbol string learning pronunciation dictionary[3]5.word n-gram word cluster in a language model[2]6.word histogram language model of a topic cluster[2]Table14.1:ASR-related SOM and LVQ applications.As a promising future alternative for acoustic models in speech recognition an active research topic in the laboratory has also been the development of continuous state-space models of speech and Bayesian ensemble learning for latent variables.For more information on these topics see the chapter corresponding to the Bayesian modeling group.Besides the recognition of speech,we have used our models also for the segmentation of new large Finnish speech corpora.The segmentation of the new data is an essential first step before the new material can be used for training.This work is related to the national USIX research program and has been helpful for the other participating Finnish speech research groups who are working on the same database.14.2Language modelingThe output of the phonemic vocabulary-free recognizer will inevitably contain some errors. Our current research is focused towards large vocabulary continuous speech recog-nition(LVCSR)systems and language modeling.The role of the language model is to control the search of the best phoneme or word sequence and improve the recognition. Since the best modeling methods are language specific,we cannot simply use the same models which have given good results e.g.for English.We have to cope with the special characteristics of the Finnish language which include e.g.a relatively free word order and a very large recognition vocabulary due to the number of inflected word forms and compound words.Some these problems are common to other non-english languages as well.In order to better test the new methods and algorithms we are currently developing a new efficient decoder for the LVCSR task.This will be integrated in our speech recognition system.One research topic has been how to better estimate the parameters of a language model.Since the models can consist of tens of millions of parameters,the parameter estimation is very sensitive to training methods and peculiarities of the training data.By carefully compressing the language model down to much fewer parameters,we increase the model’s robustness and its ability to generalize for unseen case.One way to reduce the parameter count is to cluster similar words to one cluster and operate on these clusters instead of individual words[2].An emerging new research topic is the use of the efficient language processing tools developed in the laboratory(WEBSOM)to organize language models based on the topical structure of the discourse[2].The objective is to increase the language modeling accuracy and to obtain improved speech recognition results by automatically detecting and focusing into the best available language model for the recognition task at hand.This work is done in a close collaboration with the Natural language modeling group(see Section13.2). References[1]ing Self-Organizing Maps and Learning Vector Quantization for MixtureDensity Hidden Markov Models.PhD Thesis.Helsinki University of Technology,Neural Networks Research Centre,1997.[2]V.Siivola,M.Kurimo,and rge vocabulary statistical language model-ing for continuous speech recognition in Finnish.In Proceedings of the7th European Conference on Speech Communication and Technology,volume1,pages737–730,2001.[3]P.Somervuo.Self-Organizing Maps for Signal and Symbol Sequences.PhD Thesis.Helsinki University of Technology,Neural Networks Research Centre,2000.。

语音识别的定义,发展历程,基本原理和应用一、语音识别（voice recognition，speech recognition）的定义是：让机器通过识别和理解，将人的语音信号转换为相应的文本或命令的过程。

语音识别是以语音为研究对象，通过语音信号处理和模式识别让机器自动识别和理解人类口述的语言的技术。

语音识别是一门多学科交叉技术，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。

语音识别的本质是基于语音特征参数的模式识别，即通过学习，系统能够把输入的语音按一定模式进行分类，进而依据判定规则找出最佳匹配结果。

二、语音识别技术的发展历程可以分为以下几个阶段：1.20世纪50年代：这是语音识别的起步阶段，主要研究基于各种不同的语言特性，提取特征参数。

2.20世纪60年代：在这个阶段，研究者开始关注更具体的语言知识，包括句法、语义等，开始利用更复杂的信息来进行语音识别。

3.20世纪70年代：研究者们开始开发大型的语音数据库和语音识别的相关算法。

4.20世纪80年代：随着计算机技术的发展，语音识别的精度和效率得到了显著提高。

5.20世纪90年代：随着人工智能技术的兴起，语音识别技术得到了进一步的发展和应用。

6.21世纪：随着深度学习技术的发展，语音识别技术取得了重大突破，可以处理更加复杂和大规模的语音数据。

三、语音识别的基本原理：语音识别技术的基本原理是将人类语音信号转换为数字信号，然后通过计算机算法进行分析和处理，最终将其转换为文本或命令。

具体来说，语音识别系统通常包括以下步骤：声音信号的采集、预处理、特征提取、模式匹配和后处理等。

其中，模式匹配是语音识别的核心部分，它通过将输入的语音信号与预先训练好的模型进行比较，找到最匹配的模型，从而得到对应的文本或命令。

四、语音识别技术的应用非常广泛，包括但不限于以下几个方面：二、语音助手：这是语音识别技术在生活中的一个重要应用。

Speech Recognition TechnologySummarizeVoice recognition technology, also known as automatic speech recognition Automatic Speech Recognition, (ASR), whose goal is the human voice in the content words into a computer readable input, such as keys, binary code or sequence of characters. And speaker recognition and speaker verification is different from the latter try to identify or confirm the issue and not one voice of the speaker's words contain the content. Speech recognition technology, including voice dialing, voice navigation, control room equipment, voice document retrieval, data entry and other simple dictation. Voice recognition technology and other natural language processing techniques such as machine translation and speech synthesis technology combine to build a more complex applications, such as voice to voice translation. Speech recognition technology in the fields of: signal processing, pattern recognition, probability theory and information theory, vocal mechanism and hearing mechanism, artificial intelligence and so on.HistoryBefore the invention of early computer, the idea of automatic speech recognition has already been put on the agenda, early vocoder can be regarded as speech recognition and synthesis of the prototype. The 1920 production of "Radio Rex" toy dog may be the first speech recognition, and when the dog's name is calling when it can pop up from the base. The earliest computer-based speech recognition system is composed of AT & T Bell Labs developed Audrey speech recognition system, which can identify the number of 10 in English. The speech recognition method is to track the resonance peak. The system has been a 98% accuracy. To the late 1950s, the London College (Colledge of London) The Denes has joined the Speech Recognition Grammar probability. In the 1960s, artificial neural networks are introduced speech recognition.The two major breakthroughs in this era is the LPC Linear Predictive Coding (LPC), and dynamic time warping Dynamic Time Warp technology. Speech recognition technology is the implied most significant breakthrough in Markov model Hidden Markov Model of the application.Baum made from the relevant mathematical reasoning, and others through Labiner research, Carnegie Mellon University, Kai-fu Lee finally realized the first Hidden Markov Model based on large vocabulary speech recognition system Sphinx. [1]. Since then, strictly speaking, voice recognition technology is not out of HMM framework. Although researchers have been trying for years to "Dictation Machine" promotion, voice recognition technology in the field yet could not support unlimited, unlimited application of the speaker's dictation machine.ModelAt present, the mainstream of large vocabulary speech recognition systems use statistical pattern recognition technology and more. Typical statistical pattern recognition method based on speech recognition system consists of the following modules constitute the basic signal processing and feature extraction module. The module's main task is to extract features from the input signal for the acoustic model processing. At the same time, it generally also includes a number of signal processing technology to minimize environmentalnoise, channel, speaker and other factors on the characteristics of impact.Statistical acoustic model. Typical systems use more first-order hidden Markov model based modeling. Pronunciation dictionary.Pronunciation dictionary contains the system can handle the vocabulary and pronunciation.Pronunciation dictionary to provide a real acoustic modeling and language modeling element mapping between elements. Language model. Language model of the system for the modeling language. Theory, including the regular languages, context-free grammar language models, including a language model can be, but the systems still widely used N-gram based on statistics and its variations. Decoder. Decoder is one of the core speech recognition system, its task is the input signal, according to the acoustic, language model and dictionary, to find the maximum probability be able to export the signal of the word string. From the mathematical point of view can be more clear understanding of the relationship between the modules. First, the basic statistical speech recognition problem is that the input signal or the characteristics of a given sequence of symbols set (dictionary), solving the symbol string so: W = argmaxP (W | O) through the Bayesian formula, the type can be rewritten as for the determination of the input O, P (O) is determined, so omitted it will not affect the type of final results, therefore, discussed in general speech recognition problem can be expressed with the following formula, it can beThe basic formula is called speech recognition. W = argmaxP (O | W) P (W) from this perspective, the signal processing module provides the input signal pre-processing, that is, provided the voice signal from the collection (denoted as S) to the sequences of O mapping. The acoustic model itself defines some more promotion of acoustic modeling unit, and provides features in a given input, the estimated P (O | uk) method. In order to string acoustic modeling unit mapped to the symbol set, we need to play a role in pronunciation dictionary. It actually defines the mapping of mapping. For that convenience, you can define a complete works to the Cartesian product of U, while the pronunciation dictionary is that a subset of Cartesian product.And there: Finally, the language model provides the P (W). This basic formula can be written in more specific: for the decoder to which is to be in the,, ui, and the time scale t sheets into the search space, locate the type specified in the W. Speech recognition is a cross-discipline, speech recognition, information technology is becoming the key technology in human-computer interfaces, voice recognition and voice synthesis technologies enable people to get rid of the keyboard, through voice commands to operate. Speech technology has become a competitive emerging high-tech industries. Voice communication with the machine, the machine understand what you say, this is one long dream to do. Speech recognition technology is to make the machine through the process of identifying and understanding the voice signal into text or commands corresponding high-tech. Speech recognition is a cross-disciplinary. Past two decades, speech recognition technology has made significant progress, starting from the laboratory to the market. It is expected that the next 10 years, speech recognition technology into the industrial, household appliances, communications, automotive electronics, medical care, family services, consumer electronics and other fields. Voice recognition dictation machine applications in some areas by the U.S. media as the computer 1997, the development of one of 10 events. Many experts believe that the voice recognition technology in 2000 to 2010 ten important area of information technology development technologies.First, the history of speech recognition(1) The history and status of foreign studySpeech recognition research can be traced back to the 20th century 50's AT & T Bell Laboratories Audry system, it is the first English number ten can identify the voice recognition system. But the real substantive progress, and as an important research topic is in the late 60s early 70s. This is primarily because the development of computer technology for the realization of speech recognition to provide the hardware and software may be more important is the voice signal of linear predictive coding (LPC) technology and dynamic time warping (DTW) technique proposed an effective solution to the speech feature extraction and matching of unequal length.Speech recognition in this period is mainly based on template matching principle, limited to specific areas of study were small vocabulary isolated word recognition, implemented based on linear prediction cepstrum and DTW technology-specific isolated word speech recognition system; also proposed the vector quantization (VQ) and Hidden Markov Model (HMM) theory.With the expansion of application fields, small vocabulary, specific people, isolated word speech recognition, etc. These constraints need to relax, at the same time brought many new problems: first, the expansion of vocabulary makes the selection of templates and the establishment of any difficulty; second, continuous speech, the various phonemes, syllables and words have no clear boundaries, the existence of various sound units strongly influenced by the context of co-articulation (Co-articulation) phenomenon; third, non-specific recognition When different people say the same if the corresponding acoustic characteristics are very different, even if the same person at different times, the physical, psychological state, said the same content, then there will be very different; fourth, recognition speech in background noise or other interference. Therefore the original template matching method is no longer applicable. Speech recognition research laboratory big breakthrough came in the 20th century the late 80s: it was finally broken in the laboratory large vocabulary, continuous speech, and the three major barriers to non-specific people, for the first time these three features are integrated into a system , the more typical is the Carnegie Mellon University (CarnegieMellonUniversity) of the Sphinx system, it is the first high-performance speaker-independent, large vocabulary continuous speech recognition system. During this period, the further deepening of voice recognition, the notable feature of HMM models and artificial neural network (ANN) in the successful application of speech recognition. HMM model widely used thanks to AT & TBell the efforts of scientists and other laboratory Rabiner, they had Frustrated HMM pure mathematical model engineered to researchers for more knowledge and understanding, so that statistical methods of speech recognition technology into the mainstream . Statistical Methods researcher's attention from the micro to macro, not deliberately pursue refinement of speech features, but more from the overall average (statistical) point of view to create the best voice recognition system. In the acoustic models to Markov chain based modeling speech sequences HMM (Hidden Markov Chain-type) more effectively address the short-time speech signal stability, a long time to time-varying characteristics, and some of the basic model according to unit structure into model of continuous speech sentences to achieve a relatively high modeling accuracy and modeling flexibility. In the language level, by statistical real corpus of words now between the probability that the same statistical model to distinguish between N-fuzzy tone and bring recognition homonym. In addition, the artificial neural network method,based on grammatical rules of language processing mechanisms have also been applied to speech recognition. Early 90s of the 20th century, many famous companies such as IBM, Apple, AT & T and NTT are on the speech recognition system of the practical to invest heavily.Speech recognition technology has a very good assessment mechanism, that is, the accuracy of identification, while the target in the 20th century, the late 90's in the research laboratory has been continuously improved. More representative of the system: IBM ViaVoice and DragonSystem introduced by the company's NaturallySpeaking, Nuance's NuanceVoicePlatform voice platform, Microsoft's Whisper, Sun's VoiceTone so. IBM, which developed in 1997, Chinese ViaVoice voice-recognition system can identify the following year they developed the Shanghai dialect, Cantonese and Sichuan dialect accent places such as voice recognition system ViaVoice'98. It comes with a basic vocabulary of 32,000 words, can be extended to 65,000 words, including commonly used term of office, with "error correction mechanism", the average recognition rate of 95%. The news speech recognition system with high accuracy, is the representative of Chinese continuous speech recognition system.(2)Domestic research history andSpeech recognition research in China started in the fifties, but has developed rapidly in recent years. The level of research gradually from laboratory practical. Since 1987 the implementation of the national 863 program, 863 computer expert intelligent speech recognition technology for the specific project, rolling once every two years. My voice recognition technology in the research has been largely with foreign synchronization, in Chinese speech recognition technology also has its own characteristics and advantages, and reached the international advanced level. Chinese Academy of Sciences Institute of Automation, Institute of Acoustics, Tsinghua University, Peking University, Harbin Institute of Technology, Shanghai Jiaotong University, China University of Technology, Beijing University of Posts and Telecommunications, Huazhong University of Science and other research institutions have conducted laboratory research in speech recognition, which has representatives research unit of Electronic Engineering, Tsinghua University and Chinese Academy of Sciences Institute of Automation, State Key Laboratory of Pattern Recognition. Electronic Engineering, Tsinghua University, speech technology and ASIC design discussion group, who developed non-specific string of Chinese continuous speech recognition system of digital identification accuracy, 94.8% (variable length string of numbers) and 96.8% (fixed length string of numbers). In a 5% rejection rate case, the system recognition rate of 96.9% (variable length string of numbers) and 98.7% (fixed length string of numbers), which is one of the international best recognition results, its performance is close to practical level. 5000 R & D term packet checking independent continuous speech recognition system to achieve 98.73% recognition rate, recognition rate of 99.96% the first three elections; and can recognize words in Mandarin and Sichuan, two languages, practical use. Chinese Academy of Sciences Institute of Automation and their mode of Technology (Pattek) issued in 2002 jointly launched their different computing platforms and applications for the "day language" Chinese voice products - PattekASR, the end of the Chinese speech recognition products since 1998 has been monopolized by foreign companies history.Second, the classification of speech recognition systemSpeech recognition system can be restricted based on the input speech to be classified. From the speaker recognition system and the relevance of considerations, identification systems can be divided into three categories: (1) independent speech recognition system: onlyconsidered for the hand of the voice identification; (2) non-specific human speech system: Recognition Speech has nothing to do with people, usually a large number of different people to use voice recognition system to learn database; (3) more than the recognition system: a group of people usually recognize the voice, or a specific group of speech recognition system, which requires only on to identify the voice of that group of people for training. If you speak the way from the consideration of recognition system can also be divided into three categories: (1) isolated word speech recognition system: isolated word recognition system for the importation to pause after each word; (2) Linking voice recognition systems: Linking Enter the system requirements on the pronunciation of each word clearly, some even began to sound phenomena; (3) continuous speech recognition system: continuous speech input is a natural fluent continuous speech input, a large number of even tone and inflection occur. From the recognition system vocabulary size considerations, can also identify system is divided into three categories: (1) small vocabulary speech recognition system.Typically include dozens of words in speech recognition systems. (2) medium vocabulary speech recognition system. Usually include hundreds of thousands of words to word recognition system.(3) large vocabulary speech recognition system. Usually includes several thousand to tens of thousands of words of speech recognition systems. As computer and digital signal processor computing power, and improve the accuracy of recognition systems, recognition systems are classified according to the size of vocabulary is also constantly changing. Is now the medium vocabulary recognition systems into the future may be small vocabulary speech recognition system. These various restrictions determined the difficulty of speech recognition system.Third, several basic methods of speech recognitionIn general, the speech recognition in three ways: knowledge-based channel model and voice methods, template matching method and the use of artificial neural network approach. (1), and acoustic phonetics-based approachThe method is started early, raised in the beginning of speech recognition technology, there is research in this area, but the model and the voice of knowledge is too complicated for practical use at this stage no. Usually that common language has a finite number of different voice-based element, but also through its speech signal in frequency domain or time domain characteristics to distinguish. The method is divided into two steps to achieve this: first, segmentation and labeling of the speech signal time-divided into discrete segments, each corresponding to one or a few primitive acoustic characteristics of voice. Then according to the acoustic characteristics corresponding to each sub-step is given a similar speech label, get the word sequence from speech under the first label to get a voice-based sequence element mesh, the effective word from the dictionary sequence can also be combined with the sentence grammar and semantics at the same time.(2) template matching methodTemplate matching method to develop more mature, has reached a practical stage. In the template matching method, to go through four steps: feature extraction, template training, templates, categories, judgments. There are three common techniques: dynamic time warping (DTW), Hidden Markov (HMM) theory, vector quantization (VQ) technology. 1, dynamic time warping (DTW) endpoint detection of speech signals for speech recognition is a fundamental step, which is the basis for training and recognition characteristics. The so-called endpoint detection is the various paragraphs of the speech signal (such as phoneme, syllable, morpheme) the start point and end point, from the voice signal to exclude silent segment. In the early days, the main basis for endpoint detection is the energy, amplitude and zero crossing rate. But the effect is often not obvious.60's Japanese scholars Itakura proposed the dynamic time warping algorithm (DTW: DynamicTimeWarping). Idea of the algorithm is the unknown quantity rise uniform length or shorter, until the same length as the reference model. In this process, the unknown words to distort the timeline to uneven or curved, to make it on the positive features and model features. 2, Hidden Markov Method (HMM) Hidden Markov Method (HMM) speech recognition of 70 years to introduce the theory, it appears so natural speech recognition system achieved substantial breakthrough. HMM speech recognition method has become the mainstream technology, most current large vocabulary, continuous speech speaker-HMM speech recognition systems are based on the model. HMM is a voice signal of the time sequence established statistical model, be viewed as a mathematical double random process: one is to use a finite state Markov chain to simulate a number of statistical properties of speech signal changes in the implied stochastic process, and the other A Markov chain is associated with each state of the observation sequence of random process. The former by the latter demonstrated, but the specific parameters of the former is unpredictable. The words of the process is actually a double stochastic process, speech signal itself is an observable time-varying sequence, is the brain according to knowledge of grammar and language needs (unobservable state) parameters given phoneme stream. HMM shows a reasonable imitation of this process, a good description of the speech signal of the overall non-stationarity and local stationarity is a more satisfactory model of a voice. 3, vector quantization (VQ) vector quantization (VectorQuantization) is an important signal compression method. Compared with HMM, vector quantization is mainly applied to small vocabulary, isolated word speech recognition. The process is: the voice signal waveform of the k-samples of each frame, or k parameters for each parameter frame, form a k-dimensional vector space, and then vector to quantify. Quantified, it will be k-dimensional infinite space is divided into M regions boundary, then the input vectors are compared with those boundaries, and was to quantify the "distance" the center of the boundary of the smallest value of the vector. Vector quantizer design is from a large number of signal samples to train a good codebook, starting from the actual results to find a good distortion measure defined formula, the optimal design vector quantization system, search and computation with the least amount of distortion of the operation to achieve the maximum possible SNR. Core idea can be understood: If a code book for a particular source and the optimal design, then sources of information generated from this signal and the codebook should be less than the average quantization distortion to the signal and the other information The average quantization distortion codebook, that there is discrimination between the encoder itself. In the actual application process, it also studied the method for reducing complexity, these methods can be divided into two categories: non-memory vector quantization and vector quantization with memory. No memory of tree search vector quantization, including vector quantization and multi-stage vector quantization.(3) neural network approachArtificial neural network approach is proposed by the late 80s, a new speech recognition method. Artificial neural network (ANN) is essentially an adaptive nonlinear dynamic system modeling principles of human neural activity, self-adaptability, parallelism, robustness, fault tolerance and learning characteristics, the classification of its strong ability to and input - output mapping capabilities in speech recognition are very attractive. But because of training, identify the shortcomings too long, is still at an experimental stageof exploration.As the ANN is not a good time to describe the dynamic characteristics of voice signals, so often the combination of ANN with the traditional methods, respectively, the respective merits of using voice recognition.Fourth, the structure of speech recognition systemA complete speech recognition system based on statistics can be broadly classified into three parts: (1) Speech signal preprocessing and feature extraction; (2) acoustic model and pattern matching; (3) language model and language processing,(1) Speech signal preprocessing and feature extractionSelective recognition unit is the first step in speech recognition research. Speech recognition unit has the word (sentence), syllable and phoneme are three specific options which, by the decision of the specific research tasks. Word (sentence) elements are widely used in small and medium vocabulary speech recognition system, but not suitable for large vocabulary system, because the model library is too large, the heavy task of training models, model matching algorithm complexity, real-time requirements difficult to meet. Syllable units more common in Chinese speech recognition, mainly because Chinese is a monosyllabic structure of the language, English is the multi-syllable, and although there are about 1,300 Chinese syllables, but fails to consider tone, about 408 non-stressed syllables, the number of relatively less.Therefore, in large vocabulary speech recognition systems, to identify syllable as the basic unit is feasible. Phoneme units previously more common in English speech recognition research, but, the large vocabulary speech recognition systems are increasingly used. The reason is that Chinese syllables only by the initials (including zero consonant 22) and finals (total 28) form, and we classify the acoustic characteristics of a big difference. Often been applied by the initials follow-finals of the different composition refinement of initials, so although the increase in the number of model, but increase the ability to distinguish between easily confused syllables.As the co-articulation of phoneme unit unstable, so how to obtain a stable phoneme units, there are to be studied. Speech recognition is a fundamental problem is a reasonable choice of features. Feature extraction aims to analyze the speech signal processing, speech recognition has nothing to do with removing the redundant information, access to important information affecting speech recognition, speech signal is compressed at the same time. In practice, the voice signal between the compression rate is between 10-100. Speech signal contains a large variety of different information, what information extraction, extraction, in what way, is necessary to consider various factors, such as cost, performance, response time, computation and so on.Non-independent speech recognition systems are generally focused on extracting the characteristic parameters reflecting the semantics, as far as possible to remove the speaker's personal information; and independent speech recognition system is hoped to reflect the semantic feature extraction parameters as far as possible also include the speaker's personal information . Linear prediction (LP) analysis is widely used feature extraction techniques, many successful applications are extracted based on LP technology cepstrum. But the linear prediction model is purely mathematical model, without considering the human auditory system's processing of voice characteristics. Mel parameters and linear prediction based on perceptions (PLP) analysis of the extracted perceptual linear prediction cepstrum, to a certain extent, to simulate the human ear to the voice processing features,application of human auditory perception of some aspects of research. Experiment shows that using such technology, voice recognition system to improve the performance of certain.From the current situation, Mel scale inverted spectrum parameters have gradually replaced the original common LPC cepstrum derived parameters, because it takes into account the human voice sound characteristics of the receiver has better Lu rods of (Robustness). The researchers also tried to apply wavelet analysis, feature extraction, but performance is difficult with this technology than to be studied further.(2)acoustic model and pattern matchingAcoustic model is usually used to access voice features of the training algorithm can be trained to produce. When the input in identifying characteristics of simultaneous voice model (the model) for matching and comparison, the best recognition results. Acoustic model is to identify the underlying system model, and is a voice recognition system, the most critical part. Acoustic model is intended to provide an effective way of calculating the feature vector of speech template sequence and the distance between each sound. Acoustic model design and features of language is closely related to pronunciation. Acoustic model unit size (the word pronunciation model, semi-syllable models or phoneme model) of the speech training data size, system identification rate, and flexibility have greater impact. Must be based on the characteristics of different languages, the size of vocabulary recognition system identification unit the size of the decision. In the Chinese language as an example: the pronunciation of Chinese characteristics according to phoneme categories are divided into consonants, sound modules, complex vowels, complex nose and tail of four, according to syllable structure for the initial and final classification. And constitute a consonant or vowel phoneme. Sometimes, the tone of the vowels with the mother called the tune. Transferred by the parent or by a single consonant and a syllable phonetic tone mother. Mandarin Chinese is a syllable of a word sounds, syllables or words. Characters to form words by the syllable, the last sentence by the word form. There are 22 initials in Chinese, including zero consonants, vowels total of 38. By phoneme classification, Chinese, a total of 22 consonants, 13 Vowels, 13 complex vowels, complex nose and tail 16.The most commonly used primitive acoustic model for the syllable, syllables or words, according to achieve different objectives to select the different primitives. Chinese modal particle with a total of 412 syllables, including the light tone words, a total of 1282 have stressed syllable word, so when the word in the small vocabulary isolated word speech recognition is often used as the base element, in large vocabulary speech recognition, syllable or sound is often used vowel model, while in continuous speech recognition, because co-articulation effects, often using fin al acoustic modeling. Speech Recognition Model Based on Statistics HMM model used is λ (N, M,。

机器人语音识别作文英语As the development of technology, speech recognition technology has been widely used in our lives. Speech recognition technology, also known as voice recognition technology, is a technology that can convert human speech into text or commands that can be recognized by machines. With the help of speech recognition technology, we can easily communicate with machines, such as smartphones, smart speakers, and robots.Speech recognition technology has greatly improved our lives. For example, when we are driving, we can use voice commands to make phone calls, send text messages, or play music without taking our hands off the steering wheel. When we are cooking, we can ask our smart speaker to play our favorite music, set a timer, or read a recipe for us. When we are watching TV, we can use our voice to change the channel, adjust the volume, or search for programs.One of the most significant applications of speechrecognition technology is in the field of robotics. Robots with speech recognition technology can understand human speech and respond accordingly. They can help us with our daily tasks, such as cleaning the house, doing the laundry, or even cooking. They can also be used in healthcare, education, and entertainment.In healthcare, robots with speech recognition technology can help doctors and nurses to take care of patients. They can remind patients to take their medicine, measure their vital signs, and provide emotional support. In education, robots with speech recognition technology can help teachers to teach students. They can answer students' questions, give feedback on their performance, and provide personalized learning experiences. In entertainment, robots with speech recognition technology can provide interactive experiences for users. They can play games, tell stories, and sing songs.However, speech recognition technology also has some limitations. For example, it may not work well in noisy environments or with people who have accents or speechimpairments. It may also have privacy concerns, as it requires access to our personal information and conversations.In conclusion, speech recognition technology has brought us many benefits and has great potential in various fields. With the continuous improvement of technology, we can expect more advanced and intelligent robots with speech recognition technology in the future. However, we should also be aware of its limitations and take measures to protect our privacy.。

自然语言处理中的语音识别与机器翻译在人工智能领域中，自然语言处理（Natural Language Processing，NLP）一直是一个热门问题，因为长期以来，计算机很难理解自然语言（人类语言）。

随着技术的不断进步，人工智能逐渐开始实现人与计算机之间的自然交流。

语音识别和机器翻译是自然语言处理领域中比较重要的两个研究方向，它们之间有着千丝万缕的联系。

一、语音识别语音识别（Speech Recognition）是将发音者语音中所含信息自动转换为文本或指令的一项技术。

在口语交互、智能客服、智能家居等领域，语音识别技术越来越受欢迎。

机器通过语音识别技术可以与用户进行自然交流，并在一定程度上替代人工劳动。

语音识别的实现基于人的耳朵如何分辨和理解人言。

语音信号通过麦克风采集，然后进行数字化处理，接下来，语音识别器会将数字化语音转化为文本，需要经历的过程主要包括语音信号预处理、特征提取、语音识别和文本后处理四个阶段。

由于语音识别必须考虑语言多样性和语音干扰问题，因此实现起来并不容易。

二、机器翻译机器翻译（Machine Translation，MT）是指通过计算机对语言进行翻译。

它是将源语言转化为目标语言的一项技术，目前，在文本翻译和口语翻译领域，机器翻译技术已经得到了广泛的应用。

机器翻译的实现通常有两种方法，分别为基于规则的机器翻译和基于统计的机器翻译。

基于规则的机器翻译是通过定义语言规则和翻译规则来进行翻译的，它对语言的掌握要求较高，偏向于使用固定模型。

相比之下，基于统计的机器翻译最初采用双语语料来进行翻译，然后依靠统计模型来自适应性地进行更新。

这种方法对语言的要求较低，但是它的翻译效果往往会受到某些限制。

三、语音识别和机器翻译的联系虽然自然语言处理的领域非常广泛，但是语音识别和机器翻译之间有着密不可分的联系，它们的联系在以下几方面体现：1. 相同的信息预处理过程。

语音信号和原文本几乎一样都需要进行预处理，以保证信息的完整性。

人机交互式机器翻译中的语音识别技术人机交互式机器翻译（Interactive Machine Translation，IMT）已经成为机器翻译领域的一个重要研究方向。

语音识别（Speech Recognition，SR）技术在IMT中扮演着关键的角色。

本文将介绍语音识别技术在IMT中的应用和挑战，并讨论当前的研究进展和未来的发展方向。

一、语音识别技术在IMT中的应用语音识别技术在IMT中可以用于两个主要的方面：输入端的语音转写和输出端的语音合成。

1. 输入端的语音转写在IMT中，用户可以通过语音输入来获取翻译结果，而不用通过键盘输入。

语音转写技术可以将用户的语音输入转化为机器可理解的文本输入。

这样，用户可以通过直接讲话的方式与机器进行交互，提高用户体验。

语音转写技术能够帮助IMT系统更好地适应多样化的用户需求，尤其是对于不外语的用户而言，使用语音输入会更加便捷。

同时，语音转写技术也使得IMT系统可以应对较强的环境噪音和语音识别错误的情况。

2. 输出端的语音合成除了语音转写，语音识别技术还可以用于输出端的语音合成。

一旦机器完成翻译过程，可以通过语音合成技术将翻译结果转化为语音输出，以便用户更方便地获取翻译结果。

语音合成技术可以实现个性化的语音输出，包括声音的音调、语速、语音风格等。

通过语音合成技术，IMT系统可以使得用户获取翻译结果的方式更丰富，满足不同用户的需求。

二、语音识别技术在IMT中的挑战尽管语音识别技术在IMT中有着广泛的应用，但是仍然面临着一些挑战。

1. 音频质量和环境噪音语音识别技术对音频质量和环境噪音非常敏感。

如果音频质量较差或者环境噪音较大，语音识别的准确率会大幅下降。

这对于IMT系统而言是一个严峻的挑战，因为用户的语音输入存在很大的不确定性。

如何处理音频质量较差和环境噪音较大的语音输入是IMT系统中需要解决的关键问题。

2. 多语种识别IMT系统需要支持多种语言的翻译，并且用户可以使用不同的语言进行语音输入。

自然语言处理中的名词解释自然语言处理（Natural Language Processing，简称NLP）是人工智能领域中一项关乎人与计算机之间使用自然语言进行交互的技术。

随着人工智能技术的不断进步，NLP带来了许多令人兴奋的应用和突破，如机器翻译、语音识别、情感分析等。

本文将从这些应用的角度出发，对自然语言处理中的一些关键概念进行解释。

首先，我们来谈谈机器翻译（Machine Translation）。

机器翻译是指使用计算机来将一种自然语言翻译成另一种自然语言的技术。

在过去的几十年里，机器翻译经历了从基于规则的方法到基于统计的方法再到如今的神经网络模型的演变。

其中，神经网络模型的出现可以说是机器翻译领域的一大突破，它通过大量的语料训练，使得机器翻译的效果大幅提升。

接下来是语音识别（Speech Recognition）。

语音识别是一种将说话人的语音转换成文字的技术。

早期的语音识别系统主要基于声学模型和语言模型，但这些方法在句子长度较长、语速较快等情况下容易出现错误。

近年来，随着深度学习技术的发展，端到端的语音识别模型逐渐兴起。

这种模型直接将声音信号作为输入，输出对应的文本结果，简化了传统方法中的多个步骤，从而取得了更好的效果。

此外，还有情感分析（Sentiment Analysis）。

情感分析是指对文本中所包含的情感进行分析和识别的过程。

情感分析常用于对用户的评论、社交媒体内容等进行情感评价。

它可以分为三个主要任务：情感极性分类、情感强度量化和情感目标识别。

情感分析的应用广泛，包括市场调研、舆情监测、产品推荐等领域。

另外，问答系统（Question Answering）也是自然语言处理的重要应用之一。

问答系统旨在通过自动回答用户提出的问题，通常涉及信息检索、知识图谱、文本理解等相关技术。

它可以帮助用户快速找到所需的信息，提高信息检索的效率。

问答系统的研究面临着许多挑战，如语义理解、知识获取和推理等。

机器翻译中的语音识别和语音翻译技术机器翻译（Machine Translation，MT）技术是一种将一种自然语言文本自动翻译成另一种自然语言文本的技术。

它的发展历程可以追溯到20世纪50年代，始于人工智能的研究，并在计算机技术的发展和统计机器翻译（Statistical Machine Translation，SMT）的出现之后得到了突飞猛进的发展。

而在机器翻译中，语音识别（Speech Recognition）和语音翻译（Speech Translation）技术则起着至关重要的作用，为机器翻译提供了从口语输入到文本翻译的完整解决方案。

语音识别技术是将口语输入转换为文本的过程。

它的发展经历了几个重要的阶段。

早期的语音识别技术主要基于规则，将声音信号与模型进行比较，并通过匹配来选择最接近的文本结果。

然而，这种方法需要大量的规则和人工干预，无法应对海量的语音数据。

随着机器学习和深度学习的发展，统计语音识别（Statistical Speech Recognition）和基于深度学习的语音识别（Deep Learning-based Speech Recognition）技术逐渐兴起。

这些技术使用大规模的语音训练数据和复杂的深度神经网络模型，通过学习声学模型和语言模型的统计规律，实现了更准确、更高效的语音识别。

语音识别技术的进步为语音翻译技术的发展提供了坚实的基础。

语音翻译技术是基于语音识别的基础上，将识别到的文本转化为另一种自然语言的过程。

它可以将口语输入直接转换为另一种语言的口语输出，大大提升了人们进行跨语言交流的便利性。

早期的语音翻译技术主要基于规则和模板匹配，需要人工编写大量的翻译规则和模板。

然而，这种方法限制了翻译的灵活性和适应性，并且需要大量的人力资源进行维护和更新。

随着机器学习和深度学习的发展，统计机器翻译（Statistical Machine Translation，SMT）和神经机器翻译（Neural Machine Translation，NMT）逐渐成为主流。

语音识别技术：改善人机交互的核心技术人工智能技术在近年来的快速发展中，人机交互成为了一个重要的研究领域。

人机交互的目标是实现人与计算机之间的有效沟通，使得计算机能够理解人类的语言和指令，并以语言方式回应。

为了达到这个目标，语音识别技术作为人机交互的核心技术，发挥了重要的作用。

语音识别技术的定义及发展历程语音识别技术（Speech Recognition）是指计算机通过分析和处理语音信号，将语音转换为可被计算机理解的文本或命令的一项技术。

语音识别技术的发展历程可以追溯到上世纪五六十年代，当时的语音识别技术仍然十分初级，准确率较低，无法实现实时的语音转换。

随着科技的不断进步，特别是深度学习和大数据技术的发展，语音识别技术取得了重大突破，准确率和性能得到了大幅提升。

语音识别技术的工作原理语音识别技术的工作原理可以简单分为三个步骤：语音信号的采集、特征提取和模型训练。

在采集阶段，计算机通过麦克风等设备获取语音信号，然后对信号进行预处理和归一化，以去除杂音和增强语音信号的质量。

在特征提取阶段，计算机会根据语音信号的频谱、波形等特征，将其转换成计算机可以理解的数字表示。

最后，在模型训练阶段，计算机通过机器学习算法，使用大量的训练数据对语音信号进行建模和分类，以提高识别准确率。

语音识别技术的应用领域语音识别技术在现代社会中应用广泛，其主要应用领域包括但不限于以下几个方面：1. 语音助手如今，许多人都在使用智能手机，而语音助手已经成为手机的标配功能之一。

通过语音助手，用户可以使用语音与手机进行交互，执行各种操作，如发送短信、拨打电话、播放音乐等。

语音识别技术的应用使得用户可以更加方便地与手机进行交互，提高了手机的用户体验。

2. 语音搜索随着互联网的发展，人们对信息的获取需求也越来越大。

语音搜索作为一种更加便捷的搜索方式，在使用手机、智能音箱等设备时得到了广泛应用。

通过语音搜索，用户只需要用语音方式提出问题或指令，计算机就能够快速地给出相关的答案或响应。

机器人说话的原理Robots are able to speak through a process called speech synthesis, which involves converting text into spoken words. 机器人能够通过语音合成的过程说话，这个过程包括将文本转换为口头语言。

The first step in this process is to input the text that the robot will speak. 这个过程的第一步是输入机器人将要说的文本。

Next, the text is analyzed by a system that determines the pronunciation and intonation of each word. 接下来，文本会被一个系统分析，该系统会确定每个词的发音和语调。

After the pronunciation and intonation are determined, the system uses a voice synthesizer to create the spoken words. 在确定了发音和语调之后，系统会使用语音合成器来创建口头语言。

The voice synthesizer takes the text and converts it into speech by generating the appropriate sounds for each word. 语音合成器会将文本转换成口头语言，通过为每个词生成合适的声音。

Finally, the spoken words are output through speakers or other audio equipment, allowing the robot to communicate with humans through speech. 最后，口头语言会通过扬声器或其他音频设备输出，使机器人能够通过语音与人类交流。

智能语音识别翻译器的技术要求智能语音识别翻译器（Intelligent Speech Recognition Translator，简称ISRT）是一种利用人工智能技术进行语音识别和实时翻译的设备或应用程序。

它能够将用户输入的语音信息转化为文字信息，并进行实时翻译，从而帮助用户更好地进行跨语言交流和理解。

现代智能语音识别翻译器的技术要求主要包括语音识别准确率、实时性、多语言支持、用户界面设计等方面。

首先，语音识别准确率是智能语音识别翻译器最基本的技术要求之一。

语音识别的准确率直接影响到翻译的质量，因此，翻译器需要能够准确地识别用户输入的语音信息，并将其转化为准确的文字信息。

为了提高语音识别的准确率，需要采用先进的语音识别算法，如深度学习等，并进行大规模的语料库训练。

其次，智能语音识别翻译器还需要具备实时性。

实时性要求翻译器能够及时地将语音信息转化为文字信息，并进行实时翻译。

为了实现实时性，需要采用高效的语音识别和翻译算法，并采用并行计算等技术。

另外，智能语音识别翻译器需要支持多语言。

在全球化的背景下，多语言支持成为智能语音识别翻译器的一个重要功能要求。

用户可以输入不同的语种的语音信息，并得到对应语种的翻译结果。

为了实现多语言支持，需要具备多语言语音识别和翻译模型，并进行相应的模型训练。

此外，智能语音识别翻译器还需要具备用户界面的友好设计。

用户界面的友好性能够提高用户的使用体验，使用户能够更方便地操作和使用翻译器。

友好的用户界面设计包括视觉界面设计和交互设计，需要考虑用户操作习惯和使用场景。

在技术实现上，智能语音识别翻译器需要采用深度学习等人工智能技术。

深度学习是一种能够模拟复杂神经网络的机器学习技术，可以通过训练大规模的语音和翻译语料库来提高语音识别和翻译的准确率。

此外，为了提高实时性和多语言支持，还可以采用并行计算、分布式系统等技术。

综上所述，智能语音识别翻译器的技术要求包括语音识别准确率、实时性、多语言支持和用户界面设计。

语音识别与自然语言处理的结合自然语言处理（Natural Language Processing，NLP）和语音识别（Speech Recognition）是两个关键的人工智能领域，它们在现代社会中扮演着重要的角色。

这两个领域在过去几十年中取得了巨大的进展，但是它们之间的结合仍然具有挑战性。

本文将探讨语音识别与自然语言处理的结合，并讨论其在实际应用中的潜力和挑战。

首先，让我们了解一下什么是语音识别和自然语言处理。

语音识别是一种将人类声音转换为文本或命令的技术。

它通过分析声波信号并将其转换为可理解和可操作的文本形式来实现这一目标。

自然语言处理则是一种通过计算机技术来理解、分析和生成人类自然语言的能力。

它涵盖了从简单单词和句子分析到更复杂的对话系统和机器翻译等领域。

将这两个领域结合起来可以产生许多有用且强大的应用程序。

首先，通过将说话人说出来的话转换为文本形式，我们可以更方便地进行文本分析和处理。

这对于从大量的语音数据中提取有用信息和知识非常有帮助。

其次，结合语音识别和自然语言处理技术可以实现更智能的对话系统。

这些系统可以理解和回应人类的自然语言输入，从而提供更好的用户体验和服务。

然而，语音识别与自然语言处理的结合也面临一些挑战。

首先，语音识别技术本身就存在一定的误差率。

这可能导致在将声音转换为文本时出现错误或不准确的结果。

这种误差可能会对后续自然语言处理任务产生不利影响。

其次，人类自然语言具有很高的复杂性和多样性，这使得理解和处理它们变得困难。

尽管自然语言处理技术取得了很大进展，但在某些情况下仍存在理解错误或歧义。

为了克服这些挑战，研究人员一直在努力改进现有技术并提出新方法来结合语音识别与自然语言处理。

一种常见的方法是使用深度学习技术来改进声学模型和文本模型，并通过联合训练来提高整体性能。

深度学习技术可以有效地处理大规模数据，并学习复杂的语言模式和特征表示。

此外，还有一些研究致力于改进语音识别和自然语言处理的特定任务，如命名实体识别、情感分析和问答系统等。

一：前沿语音识别技术是2000年至2010年间信息技术领域十大重要的科技发展技术之一。

它是一门交叉学科，正逐步成为信息技术中人机接口的关键技术。

语音识别技术与语音合成技术结合使人们能够甩掉键盘，通过语音命令进行操作。

语音技术的应用已经成为一个具有竞争性的新兴高技术产业。

二：语音识别技术概述语音识别技术，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。

语音识别是解决机器“听懂”人类语言的一项技术。

作为智能计算机研究的主导方向和人机语音通信的关键技术，语音识别技术一直受到各国科学界的广泛关注。

如今，随着语音识别技术研究的突破，其对计算机发展和社会生活的重要性日益凸现出来。

以语音识别技术开发出的产品应用领域非常广泛，如声控电话交换、信息网络查询、家庭服务、宾馆服务、医疗服务、银行服务、工业控制、语音通信系统等，几乎深入到社会的每个行业和每个方面。

三．语音识别的研究历史语音识别的研究工作始于20世纪50年代，1952年Bell 实验室开发的Audry系统是第一个可以识别10个英文数字的语音识别系统。

1959年，Rorgie和Forge采用数字计算机识别英文元音和孤立词，从此开始了计算机语音识别。

60年代，苏联的Matin等提出了语音结束点的端点检测，使语音识别水平明显上升；Vintsyuk提出了动态编程，这一提法在以后的识别中不可或缺。

机器人之语音识别（15）DEHAOz1、语音处理语音处理包括两部分：（1）一块是语音识别SR：Speech Recognition，也称为自动语音识别ASR：Automatic Speech Recognition；（2）另一块是语音合成SS：Speech Synthesis，也称为TTS：Text-To-Speech。

语音处理目前多为基于语音识别芯片的嵌入实现，也有靠软件实现的例如商用的IBM的Viavoice 和MS的SAPI以及开源的Sphinx和Julius等，都是些面向非特定人的大词汇量的连续语音识别系统。

2、语音识别语音识别，属于模式识别的一种应用，将未知的输入语音的模式和已知语音库里的参考模式逐一比较，所获得的最佳匹配的参考模式即为识别出来的结果。

语音识别目前大多是基于统计模式的，主流算法有基于参数模型的隐式马尔可夫模型HMM方法、基于人工神经网络ANN和支持向量机SVN的识别方法等。

3、识别模型（1）根据语音的机理，语音识别大框架可以分为语音层和语言层两个层级，可以理解为语音层即子音母音或者叫声母韵母，而语言层则是sequence of words。

（2）根据功能，连续语音识别系统，主要分为特征提取，声学模型训练，语言模型训练、解码器和搜索算法四大块。

更琐碎的分为：1、预处理。

滤除次要信息和背景噪声，对语音信号作端点检测从而找出语音始末位置，假设在10-30ms内语音是平稳的从而将语音分帧。

2、特征提取。

背景过滤，保留能够反映语音本质特征的信息，取出反映语音信号特征的关键参数形成特征向量序列，获得特征向量后由此特征向量通过计算余弦值可以初步获得被选的单词文本。

特征提取方法都是频谱衍生的，sphinx是用Mel频率倒谱系数MFCC。

首先用FFT将时域信号转化成频域，之后对它的对数能量谱依照Mel刻度分布的三角滤波器组进行卷积，最后对各滤波器输出所构成的向量进行离散余弦变换DCT，取其前N个系数。