In this paper we apply the maximum model distance (MMD) training to speaker identification and a new selection strategy of competitive speakers is proposed to it. The traditional ML method only utilizes the utterances for each speaker model, which probably leads to a local optimization solution. By maximizing the dissimilarities among those similar speaker models, MMD could add the discriminative capability into the training procedure and then improve the identification performance. Based on the TIMIT corpus, we designed the word and sentence experiments to evaluate this proposed training approach. The results show that the identification performance can be improved greatly when the training data is limited.
['Q. Y. Hong', 'Sam Kwong']
Discriminative training for speaker identification based on maximum model distance algorithm