Constructing the Discriminative Kernels Using GMM for Text-Independent Speaker Identification

摘要

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.