Covariance-tied clustering method in speaker identification

Abstract

Gaussian mixture models (GMMs) have been successfully applied to the classifier for speaker modeling in speaker identification. However, there are still problems to solve, such as the clustering methods. The conditional k-means algorithm utilizes Euclidean distance taking all data distribution as sphericity, which is not the distribution of the actual data. In this paper we present a new method making use of covariance information to direct the clustering of GMMs, namely covariance-tied clustering. This method consists of two parts: obtaining covariance matrices using the data sharing technique based on a binary tree, and making use of covariance matrices to direct clustering. The experimental results prove that this method leads to worthwhile reductions of error rates in speaker identification. Much remains to be done to explore fully the covariance information.