【作者】 范长青；

【导师】 华宇宁；

【作者基本信息】 沈阳理工大学 ， 检测技术与自动化装置， 2008， 硕士

【摘要】 语音识别是指让计算机能听懂人类的语言,并根据语言的内容执行一定的命令或任务,在电话拨号、家电遥控、工业控制、信息查询等领域有着广泛应用。语音识别包括孤立词语音识别、连接词语音识别、连续语音识别,本文主要研究小词汇量非特定人连续语音识别系统的开发与实现。本文详细阐述了连续语音识别系统的基本原理,研究了识别过程中的特征参数提取、模型选择和识别规则等关键技术。同时,在“硬件的软件化”思想和对信号分析处理的基础上,利用LabVIEW语言和MATLAB语言相结合的方法,开发并设计了基于虚拟仪器技术的连续语音识别系统。将虚拟仪器技术应用于语音识别系统,实现了仪器的软件化,真正体现了“软件就是仪器”的思想。从语音信号的实时采集开始,对语音信号进行预加重、小波消噪、端点检测等处理,滤除了语音信号中的无声段和噪声段,为语音特征参数的提取提供了有效的语音段,并采用美尔频率倒谱系数及其差分系数相结合的参数特征提取方法,通过矢量量化(VQ)与隐马尔可夫模型(HMM)来实现系统的训练与识别,构建了基于LabVIEW平台的非特定人连续语音识别系统。通过实验分析及其运行结果表明,利用LabVIEW开发平台构建的非特定人连续语音识别系统,对特定环境下的语音内容无需再训练,移植性好,并且语音样本容易采集,成本比较低廉,对语音内容的识别正确率达到90%左右,基本符合了实际应用的要求,具有一定的实际应用价值。更多还原

【Abstract】 Speech recognition means that computer can understand human’s speech and execute certain command or assignment according to phonetic content. It is widely used in many fields such as dial system of telephone, household appliance system of remote control, industry control, information search system and so on. It can be classified into three branches, including isolated word recognition, joint word recognition and continuous speech recognition. This paper is mainly about the research on the development and accomplishment of speaker-independent continuous speech recognition system with a small vocabulary.This paper elaborates the basic principle of continuous speech recognition system in detail, and investigates key techniques such as characteristic withdraw, model choice and verdict rule, which are adopted in identifying process. At the same time, continuous speech recognition system is developed and designed according to virtual instrument technique and on the framework of conception“the software instead of hardware”and theories of digital signal process, while LabVIEW language and MATLAB language are combined together as the method. The application of virtual instrument technique to the speech recognition system enables instrument to be softwared, embodying the thought that "the software is an instrument".With the start of real-time collection of speech signal, through the sound signal pretreatment including preweight, wavelet noise elimination and endpoint examination, silent segment and noisy segment in speech signals are eliminated. Therefore, valid speech segment for speaker feature extraction is provided. Parameter feature retrieve method of the Mel frequency cepstrum coefficient (MFCC) with its step difference coefficient is also used. Then after the system is recognized through Vector Quantization(VQ)-Hidden Markov Model(HMM), and the speaker-independent continuous speech recognition system on the platform of LabVIEW is designed .The results of experiment indicate that the continuous speech recognition system on the platform of LabVIEW has many advantages. For example, speech training is not needed in the same circumstance; it is easy to replant; speech is easy to be collected; and the cost of the system is lower. The correct rate of speech recognition is about 90%, almost up to the application requirement. So the continuous speech recognition system is proved to have practical potential.更多还原