关键词: 稀疏表征, 高斯混合模型（GMM）均值超向量, 超完备字典, 最大后验（MAP）算法

Abstract: The signal sparse theory has received more and more attentions in recent years. Sparse representation, a new classification method for speaker identification has been applied into the speaker identification system. The main idea based on this new approach is that an unknown test utterance can be represented as a linear combination of the training database while the training patterns are sufficient. According to the sparse theory, the coefficients of unknown test utterances corresponding to the class index of test models could be obtained by L1-norm minimization. Over-complete dictionary could be developed by adapting speech features to Gaussian Mixture Model-Universal Background Model（GMM-UBM） using Maximum-A-Posteriori（MAP） adaptation. This paper makes use of the sparse representation model for speaker identification, and the experiments conducted on TIMIT acoustic-phonetic continuous speech corpus show that the performance of the proposed method consistently outperforms the state of art speaker identification classifiers.

Key words: sparse representation, Gaussian Mixture Model（GMM） supervectors, over-complete dictionary, Maximum-A-Posteriori（MAP） algorithm