关键词: 说话人识别, 高斯混合模型, 说话人模型聚类（SMC）

Abstract: This paper proposes a speaker recognition method based on Speaker Model Clustering（SMC） to improve the efficiency of the recognition system. Through the calculation of an approximated Kullback-Leibler divergence, the similar speaker model is clustered. All of cluster centroid and cluster representative construct a hierarchical speaker recognition model together. During the recognition stage, the cluster is selected by calculating distance between the test vectors and cluster centroids or cluster representatives on the first step. In accordance with calculating the logarithmic likelihood between the test vectors and the speaker models in the selected cluster, the speaker is determined, with the sharp decreasement of computation. The experimental results show that the proposed method improves the recognition speed about four times and loses the accuracy rate about 0.95% compared with the traditional Gaussian Mixture Model（GMM）. In conclusion, the SMC method can improve the recognition speed with almost the same accuracy.

Key words: speaker recognition, Gaussian mixture model, Speaker Model Clustering（SMC）