At the same time according to the low recognition rate in speech recognition system , the author used the method of fundamental frequency analysis to build male / female recognition model respectively 同时针对语音识别系统中识别率不高的问题，采用基音频率分析的方法分别建造男女声识别模型。
To come true small - vocabulary speech recognition system on the dsp chip by the research about speech recognition theory and arithmetic . the result indicates that this method is feasible 作者通过对语音识别理论,算法的研究在dsp芯片上实现了小词汇量的语音识别系统,结果表明这种方法是可行的。
By taking advantage of the information retrieval technologies and the pulsar , a high performance speech document retrieval system was developed , its three major modules are speech recognition , document indexing and playback 利用自主开发的pulsar语音识别系统及信息检索技术，设计实现了一套高效的语音检索系统。
The obu could even morph into a virtual back - seat driver that does all these things and more , communicating with the driver using a synthetic voice , speech recognition and face - reading cameras Obu最终可能演化为一个随时告诉司机如何开车的乘客甚至具有更多功能，通过合成对话系统、语音识别系统和面部识别照相机，与司机进行交流。
Firstly , this paper introduces the development of speech recognition , elucidates the background and significance of the research and the difficulties we faced when we want to make it use broadly 本文首先介绍了语音识别技术的国内外发展状况，分析了语音识别系统商品化过程中面临的困难，在此基础上阐明了本课题的研究背景和意义。
The system of speech recognition is to work in the environment of peace mostly . in the environment of noise especially strong noise , the recognition rate of the system of speech recognition will get serious influenced 语音识别系统大都是在安静的环境中工作的，在噪声的环境中尤其是强噪声环境，语音识别系统的识别率将受到严重的影响。
2 . in this paper , there is brief introduction on the theory of hidden markov model ( hmm ) . in order to put hmm into practical speech recognition applications , three important problems of hmm have to be solved 2 .介绍了hmm模型的基本原理,简述了该模型的三个核心问题和解决问题的基本算法,探讨了如何将hmm模型引入实际的语音识别系统。
The noise robustness is one of the crucial factors that have deep influence upon the practicability of the speech recognition system , and then it has become the focus in the research field of automatic speech recognition 语音识别系统的噪声鲁棒性是决定语音识别技术从实验室走向实际应用的关键环节，是目前语音识别领域的研究热点与难点。
The speech recognition ' s system ( in this paper we mainly discuss ibm viavoice ) has the certain capacity of self - adaptation to the speech velocity , volume and tone , but the capacity of those is not enough with different enunciator 目前的语音识别系统(本文中主要是指ibm的viavoice语音识别系统)对语速、音量和音调都具有一定的自适应调整能力。
In an oversimplified description , the speech recognition system processes and cleans up the audio and mathematically processes and matches the audio with language and acoustic models to turn the audio stream into a set of phonemes 简而言之，语音识别系统进行处理并整理音频，然后进行数字处理并把音频与语言和声音模型匹配起来以便把音频流转为一组音素。
