Abstract：The linear prediction coefficients (LPC) are selected as features to construct the corresponding feature space. Firstly, the samples for training of each individual in system are clustered in the feature space to form initial clusters by using k-mean clustering algorithm. Then, the initial clustering results are optimized based on Gaussian mixture model (GMM) iterative algorithm to obtain the speech component unit representation of the individual. On the basis of the obtained speech component units, a text-independent speaker verification method, called averaging method, and a text-independent speaker identification method are presented. Experimental results show that the proposed algorithm can produce a satisfying result even in short utterances.
黄长存，汪增福. 一种基于语音组成单位的说话人识别算法[J]. 模式识别与人工智能, 2008, 21(6): 856-866.
HUANG Chang-Cun, WANG Zeng-Fu. A Speaker Recognition Algorithm Based on Speech Component Unit. , 2008, 21(6): 856-866.
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2008, Vol. 21 
