计算机工程与应用 ›› 2010, Vol. 46 ›› Issue (22): 162-164.DOI: 10.3778/j.issn.1002-8331.2010.22.048

• 数据库、信号与信息处理 • 上一篇    下一篇

改进的HMM和小波神经网络的抗噪语音识别

肖 勇1,覃爱娜2   

  1. 中南大学 信息科学与工程学院,长沙 410083
  • 收稿日期:2009-01-09 修回日期:2009-04-13 出版日期:2010-08-01 发布日期:2010-08-01
  • 通讯作者: 肖 勇

Noise robust speech recognition based on improved hidden Markov model and wavelet neural network

XIAO Yong1,QIN Ai-na2   

  1. College of Information Science and Technology,Central South University,Changsha 410083,China
  • Received:2009-01-09 Revised:2009-04-13 Online:2010-08-01 Published:2010-08-01
  • Contact: XIAO Yong

摘要:

通过MFFC计算出的语音特征系数,由于语音信号的动态性,帧之间有重叠,噪声的影响,使特征系数不能完全反映出语音的信息。提出一种隐马尔可夫模型(HMM)和小波神经网络(WNN)混合模型的抗噪语音识别方法。该方法对MFCC特征系数利用小波神经网络进行训练,得到新的MFCC特征系数。实验结果表明,在噪声环境下,该混合模型比单纯HMM具有更强的噪声鲁棒性,明显改善了语音识别系统的性能。

Abstract: The feature coefficients based on MFCC are not fully reflecting speech information as a result of speech signal movement and overlap of frames,especially noisy effect.A new method for noise robust speech recognition based on a hybrid model of Hidden Markov Models(HMM) and Wavelet Neural Network(WNN) is presented.The model trained by this method is used in MFCC coefficients.Experimental results show this model has better noise robustness.

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