Kernel multimodal discriminant analysis for speaker verification

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...

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Bibliographic Details
Published in2010 IEEE International Conference on Acoustics, Speech and Signal Processing pp. 4498 - 4501
Main Authors Min-Seok Kim, Il-Ho Yang, Ha-Jin Yu
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.03.2010
Subjects
Computational complexity
Covariance matrix
Feature extraction
Filtering
Kernel
Large-scale systems
Principal component analysis
Scattering
Speaker recognition
Speech enhancement
Working environment noise
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Summary: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.
ISBN:9781424442959
1424442958
ISSN:1520-6149
2379-190X
DOI:10.1109/ICASSP.2010.5495602