DocumentCode
1920384
Title
Exploiting PCA classifiers to speaker recognition
Author
Zhang, Wanfeng ; Yang, Yingchun ; Wu, Zhaohui
Author_Institution
Coll. of Comput. Sci. & Technol., Zhejiang Univ., Hangzhou, China
Volume
1
fYear
2003
fDate
20-24 July 2003
Firstpage
820
Abstract
A novel approach to text-independent speaker recognition using a new classifier, called principal component space (PCS) is proposed in this work. This classifier uses the subspaces spanned by the principal components as the criteria. Together with other PCA classifier, it forms a hybrid classifier which is another technique presented here. All of these classifiers were applied to speaker recognition in particular on YOHO corpus. The experimental works show promising results.
Keywords
neural nets; pattern classification; principal component analysis; speaker recognition; YOHO; hybrid classifiers; principal component analysis; principal component space; text-independent speaker recognition; Eigenvalues and eigenfunctions; Finite wordlength effects; Hidden Markov models; Karhunen-Loeve transforms; Principal component analysis; Space technology; Speaker recognition; Support vector machine classification; Support vector machines; Testing;
fLanguage
English
Publisher
ieee
Conference_Titel
Neural Networks, 2003. Proceedings of the International Joint Conference on
ISSN
1098-7576
Print_ISBN
0-7803-7898-9
Type
conf
DOI
10.1109/IJCNN.2003.1223488
Filename
1223488
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