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 Discriminative Weighting Method for VQ-Based Speaker Identification
AVBPA '01 Proceedings of the Third International Conference on Audio- and Video-Based Biometric Person Authentication
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
Fuzzy declustering-based vector quantization
Pattern Recognition
Hi-index | 0.00 |
In this paper, a class of VQ-based discriminative kernel is proposed for speaker identification. Vector quantization is a well known method in speaker recognition, but its performance is not superior. The distortion of an utterance is accumulated, but the distortion source distribution on the codebook is discarded. We map an utterance to a vector by adopting the distribution and the average distortions on every code vector. Then the SVMs are used for classification. A one-versus-rest fashion is used for the problem of multiple classifications. Results on YOHO in text-independent case show that the method can improve the performance greatly and is comparative with the VQ and the basic GMM's performances.