Maximum model distance discriminative training for text-independent speaker verification

This paper presents the design and implementation of text-independent speaker verification. We apply the maximum model distance (MMD) algorithm to the Gaussian mixture model (GMM) training. The traditional maximum likelihood (ML) method only utilizes the labeled utterances for each speaker model, which probably leads to a local optimization solution. By maximizing the model distance between the target and competing speakers, MMD could add the discriminative capability into the training procedure and then improve the verification performance. Based on the TIMIT corpus, we designed the verification experiments and the results show that the equal error rate (EER) could be reduced greatly compared with the traditional ML method.