弯折滤波器在说话人识别的鲁棒特征提取中的应用

Warped Filter Banks Applied in Robust Feature Extraction Method for Speaker Recognition

针对噪声环境中说话人识别性能急剧下降的问题.提出了一种用于说话人识别的鲁棒特征提取的方法.采用弯折滤波器组(Warped filter banks,WFBS)来模拟人耳听觉特性,将立方根压缩算法、相对谱滤波技术(RASTA)、倒谱均值方差归一化算法(CMVN)引入到鲁棒特征的提取中.在高斯混合模型(GMM)下进行仿真,实验结果表明该方法提取的特征参数在鲁棒性和识别性能上均优于MFCC特征参数和CFCC特征参数.

The performance of the speaker recognition system degrades drastically in the noisy environment. A robust feature extraction method for speaker recognition is proposed in this paper. Warped filter banks（WFBS） are used to simulate the human auditory characteristics. The cubic root compression method, relative spectral filtering technique（RASTA） and the cepstral mean and variance normalization algorithm（CMVN） are introduced into the robust feature extraction. Subsequently, simulation experiment is conducted based on Gaussian mixes model（GMM）. The experimental results indicate that the proposed feature has better robustness and recognition performance than the mel cepstral coefficients（MFCC） and cochlear filter cepstral coefficients（CFCC）.