本文简要介绍了语音信号处理这门研究用数字信号处理技术对语音信号进行处理的学科，它是一门新兴的交叉学科，在多门学科基础上发展起来的综合性技术。涉及到数字信号处理、模式识别、语音学、语音学、生理学、心理学及认知科学和人工智能得许多学科领域。
在语音信号处理中，隐马尔可夫模型获得了成功的应用。隐马尔可夫模型是一种既能描述语音信号的动态变化，又能很好的描述语音特征统计分布的统计模型，是准平稳时变语音信号分析和识别的有力工具。
本论文详细地介绍了隐马尔可夫模型的理论方法，突出了语音信号隐马尔可夫模型分析的重要性。并且，为了更方便的借助Matlab语言对语音进行分析，在本论文附录中加入了利用HMM的若干程序。
关 键 词
语音信号;隐马尔可夫模型;Matlab
This text synopsis introduced the speech signal handles this research handles with the arithmetic figure signal the technique proceeds to the speech signal processed of course.it is a crosses the course newly arisen, it is a synthesize technique developing on several courses foundation. Involving the arithmetic figure signal handles, the mode identifies, phonetics, phonetics, physiology, psychology and perception sciences get many courses realm with the artificial intelligence.
In speech signal handle, Hidden Markov Model acquires the successful application. Hidden Markov Model is a kind of since can describe the dynamic variety of the speech signal, again good describe the covariance model that speech characteristic statistics distribute, is an emollient tool to allow steady hour change the speech signal analysis with identify.
This thesis introduced in detail theories method of the Hidden Markov Model, outstanding the importance that Hidden Markov Model used in speech signal. And, for asking for help the language of Matlab to precede the analysis to the speech more conveniently, joined some procedures of the exploitation HMM in this thesis appendix.
Speech signal; Hidden Markov Model;Matlab1176
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