Research on the Parameter Optimal Algorithm of Gaussian Mixture Model in Speaker Identification

In the field of speaker recognition, the Gaussian Mixture Model with diagonal covariance matrices is a popular technique, in this way, it simplified model and reduced the amount of computation, but lost the correlation information between feature vectors, and then influenced the classification performance. In this paper, in order to compensate the correlation between feature elements, we proposed a novel method based on clustering transformation algorithm, we calculate the similarity between Gaussian components, and the cluster of same components will share one transformation matrix, thus multi-transformation matrices, together with weights and means vectors are obtained simultaneously by Maximum Likelihood estimation. Theory analysis and experimental results demonstrated that this proposed method can get a better balance between training speed and recognition rate, improve the performance of classifier and reduce the complexity and memory burden relatively.