Abstract: According to the randomness of determining the order of the classical Gaussian Mixture Model (GMM), affinity propagation(AP) clustering is recommended to get the order of GMM automatically. A method is proposed to recognize the speakers by applying both AP and GMM. Firstly, the speech feature parameters are extracted by combining the Mel frequency cepstrum coefficient (MFCC) with the differential cepstrum. Secondly, the affinity propagation clustering (AP clustering) method is used as the clustering of the speech feature parameters, and then the best steps of GMM are obtained automatically. On this basis, GMM model is trained. Finally, the trained GMM is used for recognizing experiment of speakers on Timit standard speech library and self-made network volunteers’ speech library. The experiment results are: the test results are 100% on Timit standard speech library and 97.6% on self-made network volunteers’ speech library in case of obtaining the order of GMM by AP clustering algorithm. There are 168 samples for training which contain 10 Chaoshan samples and 10 Hunan samples and 42 samples for testing on self-made network volunteers’ speech library. The experiment results show that the recommended AP clustering algorithm to get the order of GMM automatically can improve the accuracy and efficiency of speaker recognition significantly.

Key words: speaker recognition; mel frequency cepstrum coefficient (MFCC); affinity propagation (AP) clustering algorithm; Gaussian mixture model (GMM)