关键词: 说话人识别, K-均值奇异值分解（K-SVD）, 字典, 稀疏性

Abstract: In order to extract the personal characteristics, a speaker recognition method based on K-means Singular Value Decomposition（K-SVD） is proposed. The personal characteristics in voice can be well preserved in the dictionary trained from the K-SVD. With this feature, the dictionary which contains the personal characteristics is extracted from training data through the K-SVD algorithm. Then the trained dictionary is used for the speaker recognition. Compared to traditional methods, the personal characteristics in voice can be better preserved based on the proposed method through the sparse nature of voice and can reduce the reconstruction error. Experimental results show that the proposed method outperforms the VQ based methods for too many speakers in the view of recognition rate, so the proposed method has more practical value.

Key words: speaker recognition, K-mean Singular Value Decomposition（K-SVD）, dictionary, sparse