关键词: 说话人识别, 高斯混合模型, 美尔频率倒谱系数（MFCC）, 分裂法与K均值聚类结合法

Abstract: This paper improves the traditional method of Gaussian Mixture Model（GMM） parameters initialization at the time of GMM training.A new approach which combines division and K-means clustering is presented.The experiment shows that the proposed method can achieve the average recognition rate increase by 15.47% and 7.5% compared with the randomization and K-means clustering.At the same time，the impact of the order of GMM，covariance threshold and pre-emphasis coefficient on system recognition rate are studied.Meanwhile，the experiment results are analyzed in detail.In order to make the speaker recognition system get a higher recognition rate，their optimal values are chosen from the experiment data.The experiment shows that the system can achieve the recognition rate with above 90% under the provided experimental condition.

Key words: speaker recognition, Gaussian Mixture Modal（GMM）, Mel Frequency Cepstrum Coefficient（MFCC）, combination division and K-means clustering