Perceptual MVDR-based cepstral coefficients(PMCCs)for speaker recognition  

作　　者：LIANGChunyan ZHANG Xiang YANG Lin ZHANG Jianping YAN Yonghong 

机构地区：[1]Key Laboratory of Speech Acoustics and Content Understanding, Chinese Academy of Sciences, Institute of Acoustics, CAS Beijing 100190

出　　处：《Chinese Journal of Acoustics》2012年第4期489-498,共10页声学学报（英文版）

基　　金：supported by the National Natural Science Foundation of China(10925419,90920302, 61072124,11074275,11161140319);the Strategic Priority Research Program of the Chinese Academy of Sciences(XDA06030100)

摘　　要：A feature extraction technique named perceptual MVDR-based cepstral coefficients （PMCCs） was introduced into speaker recognition. PMCCs are extracted and modeled using Gaussian Mixture Models （GMMs） for speaker recognition. In order to compensate for speaker and channel variability effects, joint factor analysis （JFA） is used. The experiments are carried out on the core conditions of NIST 2008 speaker recognition evaluation data. The experimental results show that the systems based on PMCCs can achieve comparable performance to those based on the conventional MFCCs. Besides, the fusion of the two kinds of systems can make significant performance improvement compared to the MFCCs system alone, reducing equal error rate （EER） by the factor between 7.6% and 30.5% as well as minimum detect cost function （minDCF） by the factor between 3.2% and 21.2% on different test sets. The results indicate that PMCCs can be effectively applied in speaker recognition and they are complementary with MFCCs to some extent.A feature extraction technique named perceptual MVDR-based cepstral coefficients （PMCCs） was introduced into speaker recognition. PMCCs are extracted and modeled using Gaussian Mixture Models （GMMs） for speaker recognition. In order to compensate for speaker and channel variability effects, joint factor analysis （JFA） is used. The experiments are carried out on the core conditions of NIST 2008 speaker recognition evaluation data. The experimental results show that the systems based on PMCCs can achieve comparable performance to those based on the conventional MFCCs. Besides, the fusion of the two kinds of systems can make significant performance improvement compared to the MFCCs system alone, reducing equal error rate （EER） by the factor between 7.6% and 30.5% as well as minimum detect cost function （minDCF） by the factor between 3.2% and 21.2% on different test sets. The results indicate that PMCCs can be effectively applied in speaker recognition and they are complementary with MFCCs to some extent.

分 类 号：TN912.34[电子电信—通信与信息系统] U666.7[电子电信—信息与通信工程]