Fast training of support vector machines using sequential minimal optimization
Advances in kernel methods
Exploiting generative models in discriminative classifiers
Proceedings of the 1998 conference on Advances in neural information processing systems II
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
Speaker identification via support vector classifiers
ICASSP '96 Proceedings of the Acoustics, Speech, and Signal Processing, 1996. on Conference Proceedings., 1996 IEEE International Conference - Volume 01
Hi-index | 0.00 |
In this paper, a class of GMM-based discriminative kernels is proposed for speaker identification. We map an utterance vector into a matrix by finding the sequence of components, which have the maximum likelihood in the GMM for the all frame vectors. And the weights matrix was used, which were got by the GMM's parameters. Then the SVMs are used for classification. A one-versus-rest fashion is used for the c class problem. Results on YOHO in text-independent case show that the method can improve the performance greatly compared with the basic GMM.