改进的HMM和小波神经网络的抗噪语音识别

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。

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

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 hy- brid rnodel 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.