KERNEL MULTIMODAL DISCRIMINANT ANALYSIS FOR SPEAKER VERIFICATION

被引：3
|
作者：
Kim, Min-Seok [1 ]
Yang, Il-Ho [1 ]
Yu, Ha-Jin [1 ]
机构：
[1] Univ Seoul, Sch Comp Sci, Seoul, South Korea
来源：
2010 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH, AND SIGNAL PROCESSING | 2010年
关键词：
Feature extraction; Speaker recognition; Speech enhancement; IDENTIFICATION;
D O I：
10.1109/ICASSP.2010.5495602
中图分类号：
O42 [声学];
学科分类号：
070206 ; 082403 ;
摘要：
In this paper, we propose a robust speaker feature extraction method using kernel multimodal Fisher discriminant analysis (kernel MFDA). Kernel MFDA has been designed to have the characteristics both of kernel principal component analysis (kernel PCA) and kernel Fisher discriminant analysis (kernel FDA). Therefore, the feature vectors extracted by kernel MFDA are denoised as well as discriminated. For evaluation, we compare our proposed method with principal component analysis (PCA) and kernel PCA on the speaker verification systems.
引用
收藏
页码：4498 / 4501
页数：4
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