A novel data description kernel based on one-class SVM for speaker verification

被引：0
|
作者：
Shen, Yufeng [1 ]
Yang, Yingchun [1 ]
机构：
[1] Zhejiang Univ, Coll Comp Sci & Technol, Hangzhou 310027, Peoples R China
来源：
2007 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH, AND SIGNAL PROCESSING, VOL II, PTS 1-3 | 2007年
关键词：
speaker verification; SVM; kernel one-class SVM;
D O I：
暂无
中图分类号：
O42 [声学];
学科分类号：
070206 ; 082403 ;
摘要：
In this paper we develop a novel Data Description kernel based on One-Class SVM (OCSVM-DD kernel) used for text-independent SVM speaker verification. The basic idea of the new kernel is to combine the data description model OCSVM with SVM discriminant classifier. Utterances are firstly mapped to the normal vector of the separating hyperplane in OCSVM model. Then a SVM classifier with linear kernel is applied on those mapped vectors. Experiments results on NIST 2001 SRE database show that the performance of our new kernel is superior to Generalized Linear Discriminative Sequence (GLDS) kernel and comparative with UBM-MAP-GMM method.
引用
收藏
页码：489 / +
页数：2
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