Jialong He

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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,(More)
The Gaussian mixture speaker model (GMM) is usually trained with the expectation-maximization (EM) algorithm to maximize the likelihood (ML) of observation data from an individual class. The GMM trained based the ML criterion has weak discriminative power when used as a classifier. In this paper, a discriminative training procedure is proposed to fine-tune(More)