首页 | 本学科首页   官方微博 | 高级检索  
     检索      

噪声环境下语音识别方法研究
引用本文:吴淑珍,冯成林,黄新宇.噪声环境下语音识别方法研究[J].北京大学学报(自然科学版),2001,37(3):365-370.
作者姓名:吴淑珍  冯成林  黄新宇
作者单位:北京大学电子学系,北京,100871
摘    要:研究了6种噪声背景下与说话人有关的孤立词语音识别方法。它们是:线性预测误差法,单边自相关线性预测法,语音前端声学处理法,正则相关分析的谱变换补偿方法,特征综合法和同模极点增加法。实验结果表明,这6种方法都有效地提高了噪声环境中语音识别率,其中较好的方法在强噪声环境中(信噪比为0dB)的语音识别率达到80%以上,为信噪比较低的噪声环境中自动语音识别展现了美好前景。

关 键 词:线性预测误差  单边自相关线性预测  语音前端声学处理  正则相关分析的谱变换补偿  特征综合  
收稿时间:2000-04-05

Study on Noisy Speech Recognition Methods
WU Shuzhen,FENG Chenglin,Huang Xinyu.Study on Noisy Speech Recognition Methods[J].Acta Scientiarum Naturalium Universitatis Pekinensis,2001,37(3):365-370.
Authors:WU Shuzhen  FENG Chenglin  Huang Xinyu
Institution:Department of Electronics, Peking University, Beijing, 100871
Abstract:There are difficulties in noisy speech recognition, especially low signal-to-noise rations are more difficult. This paper describes briefly six methods for speaker-dependent noisy speech recognition(isolated words). They are LPC prediction error method, one-side auto- correlation sequence LPC, acoustic front end processing, canonical correlation based on compensation method, combination of features method and increase of poles method. The experimental results show that all the six techniques can improve effectively noisy speech recognition, and the best noisy speech recognition rate is above 80%(when SNR=0dB).
Keywords:LPC prediction error  one\|side autocorrelation sequence LPC  acoustic front end processing  canonical correlation based on compensation  combination of features
本文献已被 CNKI 维普 万方数据 等数据库收录!
点击此处可从《北京大学学报(自然科学版)》浏览原始摘要信息
点击此处可从《北京大学学报(自然科学版)》下载免费的PDF全文
设为首页 | 免责声明 | 关于勤云 | 加入收藏

Copyright©北京勤云科技发展有限公司  京ICP备09084417号