VQ-based speaker recognition has proven to be a successful method. Usually, a codebook is trained to minimize the quantization error for the data from an individual speaker. The codebooks trained based on this criterion have weak discriminative power when used as a classifier. The LVQ algorithm can be used to globally train the VQ-based classifier. However, the correlation between the feature vectors is not taken into consideration, in consequence, a high classification rate for feature vectors does not lead to a high classification rate for the test sentences. A heuristic training procedure is proposed to retrain the codebooks so that they give a lower classification error rate for randomly selected vector-groups. Evaluation experiments demonstrated that the codebooks trained with this method provide much higher recognition rates than that trained with the LBG algorithm alone, and often they can outperform the more powerful Gaussian mixture speaker models.
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