语音识别技术文献综述
基于深度学习的语音识别技术研究
基于深度学习的语音识别技术研究随着人工智能技术的发展，语音识别技术也日渐成熟。
从最初的基于模板匹配的语音识别到后来的基于统计学习的语音识别，再到今天的基于深度学习的语音识别，语音识别技术已经不再是未来科技，而是已经进入了我们的日常生活。
一、基于深度学习的语音识别技术深度学习技术是人工智能领域的热门技术之一，因其在图像识别、语音识别、自然语言处理等领域的卓越表现而备受关注。
深度学习算法通过模拟人脑的神经元网络实现对输入数据的多层抽象表示和处理。
而在语音识别任务中，深度学习算法可以通过对音频信号的建模和自适应模型训练来有效降低语音识别的误识别率。
目前基于深度学习的语音识别技术主要包括深度神经网络（Deep Neural Networks, DNNs）、卷积神经网络（Convolutional Neural Networks, CNNs）、长短时记忆网络（Long Short-Term Memory, LSTM）等多种模型。
其中，DNNs是基于前馈神经网络实现的语音识别模型，通过多个隐层抽象输入特征，将输入的音频信号映射到语音单元上，通过输出层的激活函数可以得到对音频信号的识别结果。
CNNs则是通过卷积层和池化层实现特征的提取和降维，然后再使用全连接层实现的识别。
而LSTM则是基于循环神经网络实现的模型，对于长序列信号的记忆、建模和识别效果尤为出色。
二、深度学习技术的优点相对于传统语音识别算法，深度学习技术具有以下优点：1. 非线性特征提取: 传统语音信号的特征提取通常采用Mel频率倒谱系数（Mel-frequency cepstral coefficients, MFCCs）等算法，而深度学习技术可以通过多层的非线性变换实现更为复杂的特征提取。
2. 优秀的分类性能: 深度学习算法可以通过大规模数据训练和模型自适应调整，从而获得优秀的分类性能，尤其对于噪声干扰、口音变化等情况的适应能力更强。
3. 高效的训练方法: 深度学习算法可以使用反向传播算法实现模型训练，而且可以结合GPU等并行计算技术加速训练完成。
什么是文献
在本领域内已有哪些相关工作，注 意学习先前研究所采用的方法手段.
探索作者如何分类、探索和解释事 实及其关系，提供对研究有益的思 路、方法或修改意见。
注意综述性文章 情境学习
从文献中要看出什么？
为进一步研究提供背景和基础，为 解释研究结果提供背景材料，
把握在研究中可能出现的差错，对 研ห้องสมุดไป่ตู้方案提出一些适当的修改意见， 以避免预想不到的困难。
什么是文献综述
阅读文献 创建一个统一主题 使用一个系统方式组织材料 以提纲为基础进行工作 建立起不同内容之间的桥梁
文献综述示例 信息技术 语音识别技术
文献综述主要包括的内容： 1、研究历史 2、研究现状 3、研究趋势 为自己的课题做陈述
一些需要注意的要点
特别注意期刊上的综述性文章 从参考文献向上追寻两级比较合适 对资料多的领域请细化检索的问题 对资料少的领域寻找相邻领域检索
只是对文献简单罗列，未做任何评述和分析，归纳。 没将文献与自己的研究建立联系。文献与自己的研究割裂。 要考虑文献的时效性和可靠性 。 写文献综述时，不能一味告诉别人，我读了什么，反对述而
不评，必须说明研究者对研究状况的见解，并使之成为自己 更广泛或深入研究的导引。
文献综述常见的问题
不做综述，根本没有研究文献，凭空乱写。“关于信息技术 与课程整合，在我国属于空白”。“可能/大概。。。。。”
文献与自己的研究课题不相关，或者相关性不大。通常是在 更大和更宽泛的领域做文献分析。比如研究WEBQUEST，作 者却花费了大量的篇幅和精力综述了大量的建构主义甚至更 宽泛的学习理论综述。
基于语音识别的智能语音助手技术研究与应用
基于语音识别的智能语音助手技术研究与应用
Chapter 1: Introduction 1.1 Background With the rapid development of artificial intelligence (AI) technology, intelligent voice assistants have become increasingly popular. These assistants, such as Siri and Alexa, utilize speech recognition technology to process and understand human speech, enabling users to interact with devices using natural language commands. This technology has found applications in various domains, including home automation, healthcare, customer service, and more.
1.2 Objective The objective of this research is to explore the current state of intelligent voice assistant technology, specifically focusing on speech recognition. Furthermore, we will investigate the applications and potential future developments in this field.
Chapter 2: Speech Recognition Technology 2.1 Overview of Speech Recognition Speech recognition is the technology that allows computers to convert spoken language into written text. It involves complex algorithms and models that analyze the characteristics of speech, such as phonemes, words, and sentence structures, to accurately transcribe spoken words.
科技文献综述范文
科技文献综述范文科技文献综述应由本人根据自身实际情况书写，以下仅供参考，请您根据自身实际情况撰写。
科技文献综述是对某一领域内科技文献的综合评价和总结，它可以帮助读者快速了解该领域的研究现状和发展趋势。
撰写科技文献综述需要遵循一定的结构和格式，以下是一个科技文献综述的范文，供您参考。
题目：人工智能在自然语言处理领域的应用研究综述摘要：本文对人工智能在自然语言处理领域的应用研究进行了综述，介绍了自然语言处理的基本概念、人工智能在自然语言处理领域的应用现状和未来发展趋势。
关键词：人工智能；自然语言处理；应用研究；综述一、引言自然语言处理（NLP）是人工智能领域中的一个重要分支，它涉及计算机对人类语言的处理和理解。
随着人工智能技术的不断发展，自然语言处理的应用范围越来越广泛，如语音识别、机器翻译、智能客服等。
本文旨在综述人工智能在自然语言处理领域的应用研究，介绍该领域的研究现状和未来发展趋势。
二、自然语言处理的基本概念自然语言处理是指计算机对人类语言的处理和理解，它包括语音识别、文本分析、机器翻译等多个方面。
自然语言处理的目的是让计算机能够理解和生成人类语言，从而更好地服务于人类。
三、人工智能在自然语言处理领域的应用现状目前，人工智能在自然语言处理领域的应用已经取得了很大的进展。
以下是几个典型的应用场景：1. 语音识别语音识别是自然语言处理的一个重要方面，它可以让计算机通过语音输入与人类进行交互。
目前，语音识别技术已经广泛应用于智能语音助手、语音搜索等领域。
2. 机器翻译机器翻译是指利用计算机自动将一种语言的文本转换为另一种语言的文本。
目前，机器翻译技术已经取得了很大的进展，能够实现快速、准确的翻译。
3. 智能客服智能客服是指利用人工智能技术实现自动回答用户问题的系统。
智能客服可以提高服务效率、降低成本，并提高用户体验。
四、未来发展趋势随着人工智能技术的不断发展，自然语言处理的应用前景越来越广阔。
未来，自然语言处理将会朝着以下几个方向发展：1. 多模态交互多模态交互是指将语音、图像、手势等多种模态的信息融合在一起，实现更加自然的交互方式。
开题报告范文基于深度学习的语音识别技术研究
开题报告范文基于深度学习的语音识别技术研究开题报告范文基于深度学习的语音识别技术研究1. 研究背景随着人工智能技术的不断发展，语音识别技术逐渐成为研究热点。
传统的语音识别方法面临着识别准确率低、适应性差等问题，而基于深度学习的语音识别技术则通过大量的训练数据和深层神经网络模型的设计，能够实现更高的准确率和更好的适应性。
2. 研究目的本研究旨在通过对基于深度学习的语音识别技术的研究，探索其在实际应用中的潜力和优势。
具体目的包括：（1）分析目前基于深度学习的语音识别技术的研究现状和发展趋势；（2）研究基于深度学习的语音识别技术的核心算法和模型；（3）设计并实现一个基于深度学习的语音识别系统，评估其准确率和适应性。
3. 研究内容和方法（1）研究内容文献综述的方式，系统地梳理国内外相关研究的进展；b. 研究基于深度学习的语音识别技术的核心算法和模型：重点研究深层神经网络模型、语音信号特征提取算法以及模型训练和优化方法；c. 设计并实现一个基于深度学习的语音识别系统：根据算法和模型的研究成果，结合实际需求，开发一个具有一定规模和准确率的语音识别系统；d. 评估语音识别系统的准确率和适应性：通过大量的实验和测试，对所开发的语音识别系统进行性能评估和优化，验证其在不同场景下的可行性和效果。
（2）研究方法a. 文献综述法：查阅大量文献，了解国内外学者在基于深度学习的语音识别技术方面的研究进展和趋势；b. 实验研究法：通过搭建实验平台和设计实验方案，进行数据采集和模型训练，通过实验结果进行分析和验证；c. 系统设计与实现：根据研究成果，设计语音识别系统的整体架构和模块划分，并实现相应的软件系统。
4. 预期结果及创新点（1）预期结果尽的分析和总结；b. 提出了一种基于深度学习的语音识别技术的核心算法和模型，解决了传统方法存在的问题；c. 开发了一个具有较高准确率和适应性的语音识别系统，并对其进行了评估和优化。
（2）创新点a. 研究了基于深度学习的语音识别技术的研究现状和发展趋势，掌握了该领域的最新动态；b. 提出了一种改进传统语音识别准确率和适应性的基于深度学习的方法，并进行了实验验证；c. 设计并实现了一个具有一定规模和准确率的语音识别系统，具备一定的实用性和应用前景。
语音识别 实验报告
语音识别实验报告语音识别实验报告一、引言语音识别是一项基于人工智能的技术，旨在将人类的声音转化为可识别的文字信息。
它在日常生活中有着广泛的应用，例如语音助手、智能家居和电话客服等。
本实验旨在探究语音识别的原理和应用，并评估其准确性和可靠性。
二、实验方法1. 数据收集我们使用了一组包含不同口音、语速和语调的语音样本。
这些样本覆盖了各种语言和方言，并涵盖了不同的背景噪音。
我们通过现场录音和网络资源收集到了大量的语音数据。
2. 数据预处理为了提高语音识别的准确性，我们对收集到的语音数据进行了预处理。
首先，我们对语音进行了降噪处理，去除了背景噪音的干扰。
然后，我们对语音进行了分段和对齐，以便与相应的文字进行匹配。
3. 特征提取在语音识别中，特征提取是非常重要的一步。
我们使用了Mel频率倒谱系数（MFCC）作为特征提取的方法。
MFCC可以提取语音信号的频谱特征，并且对人类听觉系统更加符合。
4. 模型训练我们采用了深度学习的方法进行语音识别模型的训练。
具体来说，我们使用了长短时记忆网络（LSTM）作为主要的模型结构。
LSTM具有较好的时序建模能力，适用于处理语音信号这种时序数据。
5. 模型评估为了评估我们的语音识别模型的准确性和可靠性，我们使用了一组测试数据集进行了模型评估。
测试数据集包含了不同的语音样本，并且与相应的文字进行了标注。
我们通过计算识别准确率和错误率来评估模型的性能。
三、实验结果经过多次实验和调优，我们的语音识别模型在测试数据集上取得了较好的结果。
识别准确率达到了90%以上，错误率控制在10%以内。
这表明我们的模型在不同语音样本上具有较好的泛化能力，并且能够有效地将语音转化为文字。
四、讨论与分析尽管我们的语音识别模型取得了较好的结果，但仍存在一些挑战和改进空间。
首先，对于口音较重或语速较快的语音样本，模型的准确性会有所下降。
其次，对于噪音较大的语音样本，模型的鲁棒性也有待提高。
此外，模型的训练时间较长，需要更多的计算资源。
多媒体应用的语音识别技术
多媒体应用的语音识别技术随着科技的快速发展，多媒体应用的使用变得越来越普遍。
语音识别技术作为一种重要的人机交互方式，在多媒体应用中起到了重要的作用。
本文将介绍多媒体应用的语音识别技术，并分析其应用场景和优势。
一、语音识别技术概述语音识别技术是指将人类的语音信息转化为文字或者命令的计算机技术。
通过对语音信号的分析和处理，计算机可以将语音转化为可读的文字或者执行相关命令。
语音识别技术主要包括语音信号的采集、预处理、特征提取和模型匹配等环节。
二、多媒体应用中的语音识别技术应用场景1. 智能助手随着智能设备的普及，人们越来越多地使用智能助手进行语音交互。
语音识别技术可以使智能助手更加智能化，可以通过语音指令进行操作，如打开软件、播放音乐等。
通过语音识别技术，智能助手可以更好地理解人们的需求，提供更加精准的服务。
2. 语音搜索语音搜索是一种越来越受欢迎的搜索方式。
通过语音识别技术，用户可以直接通过语音输入进行搜索，无需手动输入关键词。
语音搜索技术可以提升搜索的便利性和速度，使用户获得更好的搜索体验。
3. 语音录入在多媒体应用中，语音录入是一种常用的输入方式。
通过语音识别技术，用户可以通过语音进行文本的输入，如发送短信、撰写邮件等。
语音录入可以提高输入效率，避免了繁琐的手动输入。
4. 语音翻译在多媒体应用中，语音翻译是一种重要的功能需求。
通过语音识别技术，可以将其他语言的语音信息转化为文字，并进行翻译。
语音翻译技术可以帮助用户更好地理解其他语言的内容，拓宽跨文化交流的能力。
三、多媒体应用的语音识别技术优势1. 便捷高效语音识别技术可以通过语音指令实现对多媒体应用的控制，节省了操作的步骤和时间。
用户无需手动输入，只需通过语音输入即可完成相应的操作。
语音识别技术使得多媒体应用更加便捷高效。
2. 智能化交互语音识别技术可以使多媒体应用更智能化。
通过语音指令，用户可以与应用进行自然语言交互，更好地表达自己的意图。
语音识别技术可以提高多媒体应用的智能化程度，提供更个性化、智能化的服务。
中英文献翻译：语音识别speech recognition
中英文献翻译：语音识别speech recognition Speech RecognitionVictor Zue, Ron Cole, & Wayne WardMIT Laboratory for Computer Science, Cambridge, Massachusetts, USA Oregon Graduate Institute of Science & Technology, Portland, Oregon, USACarnegie Mellon University, Pittsburgh, Pennsylvania, USA1 Defining the ProblemSpeech recognition is the process of converting an acoustic signal, captured by a microphone or a telephone, to a set of words. The recognized words can be the final results, as for applications such as commands & control, data entry, and document preparation. They can also serve as the input to further linguistic processing in order to achieve speech understanding, a subject covered in section.Speech recognition systems can be characterized by many parameters, some of the more important of which are shown in Figure. An isolated-word speech recognition system requires that the speaker pause briefly between words, whereas a continuous speech recognition system does not. Spontaneous, or extemporaneously generated, speech contains disfluencies, and is much more difficult to recognize than speech read from script. Some systems require speaker enrollment---a user must provide samples of his or her speech before using them, whereas other systems are said tobe speaker-independent, in that no enrollment is necessary. Some of theother parameters depend on the specific task. Recognition is generally more difficult when vocabularies are large or have many similar-sounding words. When speech is produced in a sequence of words, language models or artificial grammars are used to restrict the combination of words.The simplest language model can be specified as a finite-state network, where the permissible words following each word are given explicitly. More general language models approximating natural language are specified in terms of a context-sensitive grammar.1One popular measure of the difficulty of the task, combining the vocabulary size and the language model, is perplexity, loosely defined as the geometric mean of the number of words that can follow a wordafter the language model has been applied (see section for a discussion of language modeling in general and perplexity in particular). Finally, there are some external parameters that can affect speech recognition system performance, including the characteristics of the environmental noise and the type and the placement of the microphone.Parameters RangeSpeaking Mode Isolated words to continuous speechSpeaking Style Read speech to spontaneous speechEnrollment Speaker-dependent to Speaker-independentVocabulary Small(<20 words) to large(>20,000 words)Language Model Finite-state to context-sensitivePerplexity Small(<10) to large(>100)SNR High (>30 dB) to law (<10dB)Transducer Voice-cancelling microphone to telephoneTable: Typical parameters used to characterize the capability of speech recognition systemsSpeech recognition is a difficult problem, largely because of the many sources of variability associated with the signal. First, the acoustic realizations of phonemes, the smallest sound units of which words are composed, are highly dependent on the context in which they appear. These phonetic variabilities are exemplified by the acoustic differences of the phoneme，At wordboundaries, contextual variations can be quite dramatic---making gas shortage sound like gash shortage in American English, and devo andare sound like devandare in Italian.Second, acoustic variabilities can result from changes in the environment as well as in the position and characteristics of the transducer. Third, within-speaker variabilities can result from changes in the speaker's physical and emotional state, speaking rate, or voice quality. Finally, differences in sociolinguistic background, dialect, and vocal tract size and shape can contribute to across-speaker variabilities.Figure shows the major components of a typical speech recognition system. The digitized speech signal is first transformed into a set of useful measurements or features at a fixed rate,typically once every 10--20 msec (see sectionsand 11.3 for signal representation and digital signal processing, respectively). These measurements are then used to search for the most likely word candidate, making use of constraints imposed by the acoustic, lexical, and language models. Throughout this process, training data are used to determine the values of the model parameters.Figure: Components of a typical speech recognition system.Speech recognition systems attempt to model the sources ofvariability described above in several ways. At the level of signal representation, researchers have developed representations that emphasize perceptually important speaker-independent features of the signal, and de-emphasize speaker-dependent characteristics. At the acoustic phonetic level, speaker variability is typically modeled using statistical techniques applied to large amounts of data. Speaker adaptation algorithms have also been developed that adapt speaker-independent acoustic models to those of the current speaker during system use, (see section). Effects of linguistic context at the acoustic phonetic level are typically handled by training separate models forphonemes in different contexts; this is called context dependentacoustic modeling.Word level variability can be handled by allowing alternate pronunciations of words in representations known as pronunciation networks. Common alternate pronunciations of words, as well as effectsof dialect and accent are handled by allowing search algorithms to find alternate paths of phonemes through these networks. Statistical language models, based on estimates of the frequency of occurrence of word sequences, are often used to guide the search through the most probable sequence of words.The dominant recognition paradigm in the past fifteen years is known as hidden Markovmodels (HMM). An HMM is a doubly stochastic model, in which the generation of the underlying phoneme string and the frame-by-frame, surface acoustic realizations are both represented probabilistically as Markov processes, as discussed in sections,and 11.2. Neural networks have also been used to estimate the frame based scores; these scores are then integrated into HMM-based system architectures, in what has come to be known as hybrid systems, as described in section 11.5.An interesting feature of frame-based HMM systems is that speech segments are identified during the search process, rather thanexplicitly. An alternate approach is to first identify speech segments, then classify the segments and use the segment scores to recognize words.This approach has produced competitive recognition performance in several tasks.2 State of the ArtComments about the state-of-the-art need to be made in the contextof specific applications which reflect the constraints on the task. Moreover, different technologies are sometimes appropriate for different tasks. For example, when the vocabulary is small, the entire word can be modeled as a single unit. Such an approach is not practical for large vocabularies, where word models must be built up from subword units.Performance of speech recognition systems is typically described in terms of word error rate E, defined as:where N is the total number of words in the test set, and S, I, and D are the total number ofsubstitutions, insertions, and deletions, respectively.The past decade has witnessed significant progress in speech recognition technology. Word error rates continue to drop by a factor of 2 every two years. Substantial progress has been made in the basic technology, leading to the lowering of barriers to speaker independence, continuous speech, and large vocabularies. There are several factorsthat have contributed to this rapid progress. First, there is the coming of age of the HMM. HMM is powerful in that, with the availability of training data, the parameters of the model can be trained automatically to give optimal performance.Second, much effort has gone into the development of large speech corpora for systemdevelopment, training, and testing. Some of these corpora are designed for acoustic phonetic research, while others are highly task specific. Nowadays, it is not uncommon to have tens of thousands of sentences available for system training and testing. These corpora permit researchers to quantify the acoustic cues important for phonetic contrasts and to determine parameters of the recognizers in astatistically meaningful way. While many of these corpora (e.g., TIMIT, RM, ATIS, and WSJ; see section 12.3) were originally collected under the sponsorship of the U.S. Defense Advanced Research Projects Agency (ARPA) to spur human language technology development among its contractors, they have nevertheless gained world-wide acceptance (e.g., in Canada, France, Germany, Japan, and the U.K.) as standards on which to evaluate speech recognition.Third, progress has been brought about by the establishment of standards for performance evaluation. Only a decade ago, researchers trained and tested their systems using locally collected data, and had not been very careful in delineating training and testing sets. As a result, it was very difficult to compare performance across systems, and a system's performance typically degraded when it was presented with previously unseen data. The recent availability of a large body of data in the public domain, coupled with the specification of evaluation standards, has resulted in uniform documentation of test results, thuscontributing to greater reliability in monitoring progress (corpus development activities and evaluation methodologies are summarized in chapters 12 and 13 respectively).Finally, advances in computer technology have also indirectly influenced our progress. The availability of fast computers with inexpensive mass storage capabilities has enabled researchers to run many large scale experiments in a short amount of time. This means that the elapsed time between an idea and its implementation and evaluationis greatly reduced. In fact, speech recognition systems with reasonable performance can now run in real time using high-end workstations without additional hardware---a feat unimaginable only a few years ago.One of the most popular, and potentially most useful tasks with low perplexity (PP=11) is the recognition of digits. For American English, speaker-independent recognition of digit strings spoken continuously and restricted to telephone bandwidth can achieve an error rate of 0.3% when the string length is known.One of the best known moderate-perplexity tasks is the 1,000-wordso-called Resource Management (RM) task, in which inquiries can be made concerning various naval vessels in thePacific ocean. The best speaker-independent performance on the RM task is less than 4%, using a word-pair language model that constrains the possible words following a given word (PP=60). More recently, researchers have begun to address the issue of recognizing spontaneously generated speech. For example, in the Air Travel Information Service(ATIS) domain, word error rates of less than 3% has been reported for a vocabulary of nearly 2,000 words and a bigram language model with a perplexity of around 15.High perplexity tasks with a vocabulary of thousands of words are intended primarily for the dictation application. After working on isolated-word, speaker-dependent systems for many years, the community has since 1992 moved towards very-large-vocabulary (20,000 words and more), high-perplexity (PP?200), speaker-independent, continuous speech recognition. The best system in 1994 achieved an error rate of 7.2% on read sentences drawn from North America business news.With the steady improvements in speech recognition performance, systems are now being deployed within telephone and cellular networks in many countries. Within the next few years, speech recognition will be pervasive in telephone networks around the world. There are tremendous forces driving the development of the technology; in many countries, touch tone penetration is low, and voice is the only option for controlling automated services. In voice dialing, for example, users can dial 10--20 telephone numbers by voice (e.g., call home) after having enrolled their voices by saying the words associated with telephone numbers. AT&T, on the other hand, has installed a call routing system using speaker-independent word-spotting technology that can detect a few key phrases (e.g., person to person, calling card) in sentences such as: I want to charge it to my calling card.At present, several very large vocabulary dictation systems are available for document generation. These systems generally require speakers to pause between words. Their performance can be further enhanced if one can apply constraints of the specific domain such as dictating medical reports.Even though much progress is being made, machines are a long way from recognizing conversational speech. Word recognition rates on telephone conversations in the Switchboard corpus are around 50%. It will be many years before unlimited vocabulary, speaker-independent continuous dictation capability is realized.3 Future DirectionsIn 1992, the U.S. National Science Foundation sponsored a workshop to identify the key research challenges in the area of human language technology, and the infrastructure needed to support the work. The key research challenges are summarized in. Research in the following areas for speech recognition were identified:Robustness:In a robust system, performance degrades gracefully (rather than catastrophically) as conditions become more different from those under which it was trained. Differences in channel characteristics and acoustic environment should receive particular attention.Portability:Portability refers to the goal of rapidly designing, developing and deploying systems for new applications. At present, systems tend tosuffer significant degradation when moved to a new task. In order to return to peak performance, they must be trained on examples specific to the new task, which is time consuming and expensive.Adaptation:How can systems continuously adapt to changing conditions (new speakers, microphone, task, etc) and improve through use? Such adaptation can occur at many levels in systems, subword models, word pronunciations, language models, etc.Language Modeling:Current systems use statistical language models to help reduce the search space and resolve acoustic ambiguity. As vocabulary size grows and other constraints are relaxed to create more habitable systems, it will be increasingly important to get as much constraint as possible from language models; perhaps incorporating syntactic and semantic constraints that cannot be captured by purely statistical models.Confidence Measures:Most speech recognition systems assign scores to hypotheses for the purpose of rank ordering them. These scores do not provide a good indication of whether a hypothesis is correct or not, just that it is better than the other hypotheses. As we move to tasks that require actions, we need better methods to evaluate the absolute correctness of hypotheses.Out-of-Vocabulary Words:Systems are designed for use with a particular set of words, but system users may not know exactly which words are in the system vocabulary. This leads to a certain percentage of out-of-vocabulary words in natural conditions. Systems must have some method of detecting such out-of-vocabulary words, or they will end up mapping a word from the vocabulary onto the unknown word, causing an error.Spontaneous Speech:Systems that are deployed for real use must deal with a variety of spontaneous speech phenomena, such as filled pauses, false starts, hesitations, ungrammatical constructions and other common behaviors not found in read speech. Development on the ATIS task has resulted in progress in this area, but much work remains to be done.Prosody:Prosody refers to acoustic structure that extends over several segments or words. Stress, intonation, and rhythm convey important information for word recognition and the user's intentions (e.g., sarcasm, anger). Current systems do not capture prosodic structure. How to integrate prosodic information into the recognition architecture is a critical question that has not yet been answered.Modeling Dynamics:Systems assume a sequence of input frames which are treated as if they were independent. But it is known that perceptual cues for words and phonemes require the integration of features that reflect the movements of the articulators, which are dynamic in nature. How to modeldynamics and incorporate this information into recognition systems is an unsolved problem.语音识别舒维都，罗恩科尔，韦恩沃德麻省理工学院计算机科学实验室，剑桥，马萨诸塞州，美国俄勒冈科学与技术学院，波特兰，俄勒冈州，美国卡耐基梅隆大学，匹兹堡，宾夕法尼亚州，美国一定义问题语音识别是指音频信号的转换过程，被电话或麦克风的所捕获的一系列的消息。
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语音识别技术综述 第 1 页 共 7 页 语音识别技术综述 The summarization of speech recognition 张永双 苏州大学 苏州 江苏
摘要 本文回顾了语音识别技术的发展历史，综述了语音识别系统的结构、分类及基本方法，分析了语音识别技术面临的问题及发展方向。 关键词：语音识别；特征；匹配
Abstact This article review the courses of speech recognition technology progress ,summarize the structure,classifications and basic methods of speech recognition system and analyze the direction and the issues which speech recognition technology development may confront with. Key words: speech recognition;character;matching
引言 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。语音识别是一门交叉学科，所涉及的领域有信号处理、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，甚至还涉及到人的体态语言（如人民在说话时的表情手势等行为动作可帮助对方理解）。其应用领域也非常广，例如相对于键盘输入方法的语音输入系统、可用于工业控制的语音控制系统及服务领域的智能对话查询系统，在信息高度化的今天，语音识别技术及其应用已成为信息社会不可或缺的重要组成部分。
1.语音识别技术的发展历史 语音识别技术的研究开始二十世纪50年代。1952年，AT&Tbell实验室的Davis等人成功研制出了世界上第一个能识别十个英文数字发音的实验系统：Audry系统。 语音识别技术综述 第 2 页 共 7 页 60年代计算机的应用推动了语音识别技术的发展，提出两大重要研究成果：动态规划(Dynamic Planning， DP)和线性预测分析(Linear Predict， LP)，其中后者较好的解决了语音信号产生模型的问题，对语音识别技术的发展产生了深远影响。 70年代，语音识别领域取得突破性进展。线性预测编码技术(Linear Predict Coding， LPC)被Itakura成功应用于语音识别；Sakoe和Chiba将动态规划的思想应用到语音识别并提出动态时间规整算法，有效的解决了语音信号的特征提取和不等长语音匹配问题；同时提出了矢量量化（VQ）和隐马尔可夫模型（HMM）理论。在同一时期，统计方法开始被用来解决语音识别的关键问题，这为接下来的非特定人大词汇量连续语音识别技术走向成熟奠定了重要的基础。 80年代，连续语音识别成为语音识别的研究重点之一。Meyers和Rabiner研究出多级动态规划语音识别算法(Level Building，LB)这一连续语音识别算法。80年代另一个重要的发展是概率统计方法成为语音识别研究方法的主流，其显著特征是HMM模型在语音识别中的成功应用。1988年，美国卡内基－梅隆大学(CMU)用VQ/HMM方法实现了997词的非特定人连续语音识别系统SPHINX。在这一时期，人工神经网络在语音识别中也得到成功应用。 进入90年代后，随着多媒体时代的来临，迫切要求语音识别系统从实验走向实用，许多发达国家如美国、日本、韩国以及IBM、Apple、AT&T、NTT等著名公司都为语音识别系统实用化的开发研究投以巨资。最具代表性的是IBM的ViaVoice和Dragon公司的Dragon Dectate系统。这些系统具有说话人自适应能力，新用户不需要对全部词汇进行训练便可在使用中不断提高识别率。 当前，美国在非特定人大词汇表连续语音隐马尔可夫模型识别方面起主导作用，而日本则在大词汇表连续语音神经网络识别、模拟人工智能进行语音后处理方面处于主导地位。 国在七十年代末就开始了语音技术的研究，但在很长一段时间内，都处于缓慢发展的阶段。直到八十年代后期，国内许多单位纷纷投入到这项研究工作中去，其中有中科院声学所，自动化所，清华大学，四川大学和西北工业大学等科研机构和高等院校，大多数研究者致力于语音识别的基础理论研究工作、模型及算法的研究和改进。但由于起步晚、基础薄弱，计算机水平不发达，导致在整个八十年代，我国在语音识别研究方面并没有形成自己的特色，更没有取得显著的成果和开发出大型性能优良的实验系统。但进入九十年代后，我国语音识别研究的步伐就逐渐紧追国际先进水平了，在“八五”、“九五”国家科技攻关计划、国家自然科学基金、国家863计划的支持下，我国在中文语音技术的基础研究方面也取得了一系列成果。在语音合成技术方面，中国科大讯飞公司已具有国际上最领先的核心技术；中科院声学所也在长期积累的基础上，研究开发出颇具特色的产品：在语音识别技术方面，中科院自动化所具有相当的技术优势：社科院语言所在汉语言学及实验语言科学方面同样具有深厚的积累。但是，这些成果并没有得到很好的应用，没有转化成产业；相反，中文语音技术在技术、人才、市场等方面正面临着来自国际竞争环境中越来越严峻的挑战和压力。 语音识别技术综述 第 3 页 共 7 页 2.语音识别系统的结构 主要包括语音信号的采样和预处理部分、特征参数提取部分、语音识别核心部分以及语音识别后处理部分，图2-1给出了语音识别系统的基本结构。
参考模式库预处理特征提取模式匹配语音信号输入语音识别基本识别结果
训练 图2-1 语音识别系统的基本结构图 语音识别的过程是一个模式识别匹配的过程。在这个过程中，首先要根据人的语音特点建立语音模型，对输入的语音信号进行分析，并抽取所需的特征，在此基础上建立语音识别所需的模式。而在识别过程中要根据语音识别的整体模型，将输入的语音信号的特征与已经存在的语音模式进行比较，根据一定的搜索和匹配策略，找出一系列最优的与输入的语音相匹配的模式。然后，根据此模式号的定义，通过查表就可以给出计算机的识别结果。
3.语音识别系统的分类 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。 语音识别技术综述 第 4 页 共 7 页 4.语音识别系统的基本识别方法 一般来说，语音识别的方法有三种：基于声道模型和语音知识的方法、模式匹配的方法以及利用人工神经网络的方法。
4.1基于语音学和声学的方法 该方法起步较早，在语音识别技术提出的开始，就有了这方面的研究，但由于其模型及语音知识过于复杂，现阶段还没有达到实用的阶段。
4.2模式匹配的方法 模式匹配方法的发展比较成熟，目前已达到实用阶段。在模式匹配方法中，需经过四个步骤：特征提取、模式训练、模式识别和判决。 4.2.1特征提取 特征提取方法主要采用以下三种： 基于LPC的倒谱参数(LPCC)分析法，基于Mel系数的Mel频标倒谱系数(MPCC)分析法，基于现代处理技术的小波变换系数分析法。在这些方法中，MFCC方法比LPCC方法的识别效果稍好一些，而且MFCC符合人们的听觉特性，在有信道噪声和频谱失真的情况下具有较好的稳健性，其不足之处是MFCC方法中多次用到FFT，故算法的复杂程度远大于LPCC方法。因此，在安静的环境下，目前比较成熟和最常用的语音特征提取方法还是LPCC方法。在条件不好的环境下，则宜选用MFCC方法。而小波变换法则是一种新兴的理论工具，要获得较高的识别率还有许多问题有待研究，但与经典的方法相比，小波变换法有着计算量小、复杂程度低、识别效果好等许多优点，研究前景十分乐观，是研究发展的一个方向。 4.2.2模式识别 模式识别常用技术有三种：动态时间规整（DTW）、隐马尔可夫模型（HMM）、矢量量化（VQ）。 (1)动态时间规整（DTW） 语音信号的端点检测是进行语音识别中的一个基本步骤，它是特征训练和识别的基础。所谓端点检测就是在语音信号中的各种段落(如音素、音节、词素) 的始点和终点的位置，从语音信号中排除无声段。在早期，进行端点检测的主要依据是能量、振幅和过零率。但效果往往不明显。上世纪60 年代日本学者Itakura 提出了动态时间规整算法。算法的思想就是把未知量均匀地伸长或缩短，直到与参考模式的长度一致。在这一过程中，未知单词的时间轴要不均匀地扭曲或弯折，以使其特征与模型特征对正。在连续语音识别中仍然是主流方法。同时，在小词汇量、孤立字(词) 识别系统中，也已有许多改进的DTW 算法提出。