基于小波调制尺度的语音特征参数提取方法

时频分析的理论基础上,提出了一种基于小波调制尺度特征的参数提取方法。根据人对调制谱信息的感知特性及干扰在调制谱中的特点,采用小波分析技术及归一化处理求得归一化的小波调制尺度特征参数,并以此作为语音的动态特征应用于语音识别系统。通过与MFCC一阶、二阶系数对比的汉语音节识别实验表明,该方法在抗噪声干扰和说话速率变化等方面比MFCC的一阶、二阶系数的性能优越,为提高语音识别鲁棒性提供了一种新途径。

Speech features extraction based on wavelet modulation scale

Based on time-frequency analysis, the theory of estimating a modulation scale representation was discussed, and a new method of features extraction for speech recognition was proposed. Considering specialty of human auditory perception and disturbances, wavelet analysis was used instead of Fourier analysis for modulation frequency transform, and wavelet modulation scales was acquired as speech features for recognition. For further attenuating the effects of disturbances, subband normalization was introduced with the wavelet modulation scales. Experiments for the Chinese syllables recognition show extracting the wavelet modulation scales as the dynamic features outperform the frequency differences both in noise environments and in time misalignment cases.