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并行模型组合方法中卷积噪声的估计方法

  • 缪彩练 ,
  • 王阳生
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  • 中国科学院自动化研究所模式识别国家重点实验室, 北京100080

收稿日期: 2002-06-25

  网络出版日期: 2003-07-10

基金资助

科技部中小企业创新基金资助项目(01C26221100019)

Estimations of Convolution Noise in PMC

  • Miao Cailian ,
  • Wang Yangsheng
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  • National Laboratory of Pattern Recognition Institute of Automation, Chinese Academy of Sciences, Beijing 100080, China

Received date: 2002-06-25

  Online published: 2003-07-10

Supported by

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摘要

由于实际环境中各种噪声的干扰,语音识别的准确率会受到不同程度的影响,因此,鲁棒性技术已成为语音识别的一个研究热点。其中,并行模型组合方法 (PMC)在提高模型对环境的适应性方面发挥着重要作用。分析了PMC中如何解决其技术难点的一些方法,并作了相应的优化改进,从而使PMC方法能适用于较为复杂的实际情况。实验利用了剑桥大学的HTK语音识别工具包,并加入自行开发的算法,可用于对 0~ 91 0个中文数字组成的数字串进行连续语音识别。结果表明,在不同的噪声环境下,新的PMC技术能显著提高识别率.

本文引用格式

缪彩练 , 王阳生 . 并行模型组合方法中卷积噪声的估计方法[J]. 中国科学院大学学报, 2003 , 20(4) : 425 -432 . DOI: 10.7523/j.issn.2095-6134.2003.4.005

Abstract

The envi ronment adaptive method plays an important part in improving the robustness of automatic speech recognition.PMC is reviewed briefly and improved to achieve bet ter performance in real adverse environment.The experiments have been done based on Cambridge' s HTK toolkit to implement the continuous Mandarin digit recognition in noisy environment

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