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中文摘要：
根据小波树稀疏性的好坏自适应分配观测数目,然后由观测数目调整小波树的节点个数,使小波树中节点数目与观测数目不匹配的问题得以解决。将预处理后的语音信号经改进小波去噪,进而通过Gammatone滤波器组,提取特征参数GFCC。在高斯混合模型下仿真实验进行。结果表明：该方法与传统非稀疏性适应观测的小波去噪方法相比信噪比提高了14%,有效削弱了语音信号中噪声的影响,且系统的识别率与鲁棒性都有明显提高。
英文摘要：
Allocate observation numbers adaptively,according to sparsity in wavelet tree of speech frames,change number of wavelet tree nodes with different observation numbers. This method solves mismatching problem between the nodes number in the tree model and measurement of speech signal. Denoising the preprocessed speech signal by improved wavelet,then,through Gammatone filters to deal with the enhanced speech signal,extract feature parameters GFCC. Simulation experiment results demonstrate that SNR increases 14 % compared with traditional wavelet method,effectively reduce effect of noise in speech signal and the system recognition rate and robustness are improved obviously.
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