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.
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2008, Vol. 21 
