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Efficient Speaker Recognition based on Multi-class Twin Support Vector Machines and GMMs

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3 Author(s)
Hanhan Cong ; Computational Intelligence Laboratory, School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, P. R. China. E-mail: ; Chengfu Yang ; Xiaorong Pu

This paper proposes a new approach for text-independent speaker recognition using twin support vector machines (TWSVMs) and feature extraction based on Gaussian mixture models (GMMs). Because of the perfect discriminability and the ability of managing large scale dataset, the proposed approach performs better than the traditional support vector machines (SVMs) on Ahumada Biometric Database and Gaudi Biometric Database.

Published in:

2008 IEEE Conference on Robotics, Automation and Mechatronics

Date of Conference:

21-24 Sept. 2008