Automatic Speech Recognition

this thesis is focused on the research topic of noise-robust front-end of automatic speech recognition ( asr). as we all know, the ultimate purpose of speech recognition is to make the computer understand human spontaneous language.

本文主要研究的是自动语音识别中的前端噪声鲁棒性问题。众所周知，语音识别的根本目的就是使机器能够听懂人类的语言。

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sonar both supports automatic speaker recognition and automatic speech recognition.

SONAR可同时支持说话人识别与语音识别。

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for more information about automatic speech recognition and directory lookups, see understanding automatic speech recognition directory lookups.

有关自动语音识别和目录查找的详细信息，请参阅了解自动语音识别目录查找。

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leo is learning about automatic speech recognition by machine.

利奥正在学习关于机器的自动语音识别。

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the common method adopted to solve this problem is to establish a language model using automatic speech recognition technology to recognize the pronunciation under test and evaluate the pronunciation capabilities.

常用的方法是采用自动语音识别技术建立语言模型，对待测语音进行识别，根据识别系数来对发音的水平进行评估。

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the error rate evaluation is very important in building an automatic speech recognition ( asr) system. the conventional algorithm for word error rate ( wer) evaluation is based on the minimum error rate.

在建立语音识别系统的过程中错误率评估起着非常重要的作用，传统的词错误率算法仅仅是基于最小错误率，具有显著的缺陷，因而不能准确评估系统的错误率。

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In this paper, the real-time realization of Automatic Speech Recognition ( ASR ) technology on common is investigated.

本文主要研究普通计算环境下语音自动识别 ( ASR ) 技术的软件 实时 实现.

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results show that ann has a higher recognition rate and potential advantages in automatic speech recognition.

研究结果表明，神经网络识别方法有较高的识别率和独特的应用优势。

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The application of Artificial Neural Networks ( ANN ) to Automatic Speech Recognition ( ASR ) is investigated in this thesis.

本文基于自动语音识别 ( ASR ) 的原理和过程,结合人工神经网络 ( ANN ) 的建模理论及特点,主要研究了人工神经网络在自动语音识别中的应用问题.

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environmental robustness is a very important issue in the field of automatic speech recognition ( asr) research.

环境鲁棒性是语音识别系统的一个很重要的问题。

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a major cause of errors in automatic speech recognition ( asr) systems is the inaccurate detection of the beginning and ending boundaries of test and reference patterns.

在语音识别系统中产生错误识别的原因之一是端点检测有误差。

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recently, computer lipreading technology has received the concern of more and more researchers, which is regarded as an auxiliary method for automatic speech recognition ( asr).

近年来，计算机唇读技术作为辅助语音识别的手段，受到越来越多的研究者的关注。

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thirdly, the application of artificial neural networks ( ann) to automatic speech recognition ( asr) is investigated in this thesis.

第三步，基于自动语音识别（ASR）的原理和过程，结合人工神经网络（ANN）的建模理论和特点，主要研究了人工神经网络在语音识别中的应用问题。

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the research of automatic speech recognition technology is an important topic in pattern recognition field, and also plays an important role in the actual application.

自动语音识别技术研究是模式识别中重要课题之一，它在实际应用中同样有着重要的作用。

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in practical automatic speech recognition systems, out-of-vocabulary ( oov) utterance detection and rejection represent an important part.

在自动语音识别系统的实际应用中，词表外（Out-of-Vocabulary，OOV）语音的检测与拒识非常重要。

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a study on noisy robust problems in automatic speech recognition by using model compensation

语音识别中基于模型补偿的噪声鲁棒性问题研究

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in recent years, with the development of automatic speech recognition techniques, small vocabulary speech recognition system which was experimented has gradually been put into practical use.

近年来，随着语音识别技术迅猛发展，小词汇量语音识别系统逐渐由实验阶段走向应用阶段。

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in this context, this thesis focuses on discriminative training of acoustic models and its application in automatic speech recognition.

在这一背景下，本文围绕声学模型区分性训练及其在自动语音识别中的应用，进行了较系统而深入的研究。

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the paper describes a chinese spoken dialogue system about real-time stock information, which integrates automatic speech recognition ( asr), natural language understanding ( nlu), dialogue management and speech generator.

介绍了一个用于股票实时行情查询的口语化的人机对话系统，该系统集成了语音识别、语言理解、对话控制等技术。

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