Discriminative training for speaker identification

Maximum model distance training is applied to speaker identification and a new selection strategy of competitive speakers is proposed. It utilises the training data more efficiently than the maximum-likelihood method. Experimental results have demonstrated that a good identification performance can be obtained even when the training data is limited.