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）

摘要： 为了提高说话人识别系统的识别效率，提出一种基于说话人模型聚类的说话人识别方法，通过近似KL距离将相似的说话人模型聚类，为每类确定类中心和类代表，构成分级说话人识别模型。测试时先通过计算测试矢量与类中心或类代表之间的距离选择类，再通过计算测试矢量与选中类中的说话人模型之间对数似然度确定目标说话人，这样可以大大减少计算量。实验结果显示，在相同条件下，基于说话人模型聚类的说话人识别的识别速度要比传统的GMM的识别速度快4倍，但是识别正确率只降低了0.95%。因此，与传统GMM相比，基于说话人模型聚类的说话人识别能在保证识别正确率的同时大大提高识别速度。

关键词: 说话人识别, 高斯混合模型, 说话人模型聚类（SMC）