A new common component GMM-based speaker recognition method

In this paper, a new common component GMM (CCGMM)-based speaker recognition approach is presented. It first defines a divergence measure to calculate the similarity of the speech signals of two speakers. Then, a CCGMM training algorithm which simultaneously maximizes the likelihood of CCGMM and the inter-speaker divergence is proposed. Performance of the proposed approach was examined using a telephone-speech database (MAT) containing 2962 speakers. A speaker recognition rate of 90.0% was achieved. The recognition rate raised to 96.1% when it was combined with the conventional GMM-based scheme.