Hierarchical mixture clustering and its application to GMM based text independent speaker identification

Abstract

In this paper, we propose a hierarchical mixture clustering method and investigate its application for complexity reduction of a GMM based speaker identification system. We show that by using GMM-HMC one can cluster speakers more accurately than that of a sorted GMM with the same acceleration rate. The system was tested on a universal background model-Gaussian mixture model with KL-divergence as the distance measure. While the proposed systempsilas performance is slightly inferior to the baseline system, its comparatively smaller computational load provides the potential to develop systems with higher performance.