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An SIPCA-WCCN method for SVM-based speaker verification system

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3 Author(s)
Yanhua Long ; Dept. of Electron. Eng. & Inf. Sci., Univ. of Sci. & Technol. of China, Anhui ; Wu Guo ; Lirong Dai

The session variability is the most important factor affecting the performance of the speaker verification systems. In order to deal with the variability more efficiently, this paper provides a practical procedure for applying a smooth within-class covariance normalization (WCCN) to an SVM-based speaker verification system, where the dimension of input samples resides in a low session-invariant principal component analysis(SIPCA) feature space. When the SIPCA and smooth WCCN approaches are implemented on NIST 2006 verification task, experimental results show relative reductions of up to 19.7% in EER and 18.4% in minimum decision cost function(DCF) over our previous GMM-mean SVM system. Our approach also has advantages in computational and memory costs compared to the state-of-art systems.

Published in:

Audio, Language and Image Processing, 2008. ICALIP 2008. International Conference on

Date of Conference:

7-9 July 2008