SVM-based text-independent speaker verification using derivative kernel in the reference GMM space

被引：1
|
作者：
Xu, Minqiang [1 ]
Dal, Beiqian [1 ]
Xu, Dongxing [1 ]
Yang, Shlqing [1 ]
Liu, Qingsong [1 ]
机构：
[1] Univ Sci & Technol, Dept Elect Sci & Technol, Hefei, Peoples R China
来源：
2008 INTERNATIONAL SYMPOSIUM ON INFORMATION PROCESSING AND 2008 INTERNATIONAL PACIFIC WORKSHOP ON WEB MINING AND WEB-BASED APPLICATION | 2008年
关键词：
D O I：
10.1109/ISIP.2008.124
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
This paper proposes a new SVM-based method for text-independent speaker verification using derivative kernel in the reference Gaussian Mixture Model(GMM) space. The model for speaker utilizes the power of SVM and GMM, reference GMM used first, and then SVM followed Using the reference GMM, not only clusters and compacts the speech, but also distinguishes the reference speaker and imposters. Then, derivative kernel combines SVM and GMM effectively. Experiments on text-independent speaker verification on NIST SRE 2001 dataset show that the Equal Error Rate(EER) of the new method is reduced to 651% from 9.88%.
引用
收藏
页码：422 / 425
页数：4
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