【作者】 涂俊辉；

【导师】 续晋华；

【作者基本信息】 华东师范大学 ， 计算机应用技术， 2010， 硕士

【摘要】 通常意义上,我们所说的语音识别指的是将语音信号转换成文字的一个过程。语音识别作为模式识别领域中一个重要的研究方向,其重要性不言而喻。语音识别技术的发展可以使得人们与计算机等设备能更方便的进行交互。其最基本的应用就是实现语音输入。语音输入可以代替键盘的功能,提高输入速度,也节省人们宝贵的时间。此外还可能将语音识别技术用来控制某些机器,汽车,飞机,手机等。本文对语音识别的一些基本理论及算法进行了一些研究和实验。首先在第二章对语音信号的处理及特征提取进行了介绍,简要的介绍了两种常见的特征提取方法,并且比较了两种特征在用于孤立词的识别时性能的差异。接下来讨论了基于隐马尔可夫模型(Hidden Markov Model)的语音识别算法。在利用隐马尔可夫模型进行孤立词识别的基础上,尝试将该模型用于英文连续词的语音识别。该部分内容中介绍了一个连续语音识别系统的构成,讨论了对声学建模单元的选取,模型参数的改进,识别算法以及统计语言模型的使用,并且介绍了一个语音识别工具HTK。利用该工具在一个大词汇量非特定人的连续语音数据库TIMIT上进行相关的实验。更多还原

【Abstract】 Generally speaking, speech recognition is a process, through which the speech signal is converted into text. It goes without saying that the research on speech recognition is of great significance, as it’s one of the important research fields in pattern recognition and has lots of application. For example, it will facilitate human’s interaction with the machines. Voice can be used as an input method, and it will save people’s time and effort when they are inputting text on a computer. Besides, speech recognition can also be used to control some machines, like automobiles, airplanes or mobile phones.This thesis introduces some theories about speech recognition and also presents the results of some experiments of improving the speech recognition algorithms. In chapter 2 we describe the processing of the speech signal and the feature extraction. We mainly focus on two types of features and make comparison between them when we are carrying out the experiment of isolated-word speech recognition. And in the next chapter, we move on to the Hidden Markov Model and its application in speech recognition. After the basic introduction of this mathematic model, we try to use it in isolated-word speech recognition. And then we continue with the continuous speech recognition using hidden markov model. The structure of a continuous speech recognition system is introduced and we also discuss several topics like, how to select the speech unit, how to improve the parameters of the hidden markov model. A toolbox called HTK and a speech database TIMIT are introduced and then they are used to carry out the experiments of large vocabulary speaker-independent continuous speech recognition.更多还原