Minimum classification error-based weighted support vector machine kernels for speaker verification

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Support vector machines (SVMs) have been proved to be an effective approach to speaker verification. An appropriate selection of the kernel function is a key issue in SVM-based classification. In this letter, a new SVM-based speaker verification method utilizing weighted kernels in the Gaussian mixture model supervector space is proposed. The weighted kernels are derived by using the discriminative training approach, which minimizes speaker verification errors. Experiments performed on the NIST 2008 speaker recognition evaluation task showed that the proposed approach provides substantially improved performance over the baseline kernel-based method. (C) 2013 Acoustical Society of America
Publisher
ACOUSTICAL SOC AMER AMER INST PHYSICS
Issue Date
2013-04
Language
English
Article Type
Article
Keywords

SPEECH RECOGNITION

Citation

JOURNAL OF THE ACOUSTICAL SOCIETY OF AMERICA, v.133, no.4, pp.307 - 313

ISSN
0001-4966
DOI
10.1121/1.4794350
URI
http://hdl.handle.net/10203/206178
Appears in Collection
EE-Journal Papers(저널논문)
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