Speaker recognition based on minimum error discriminative training

We study the use of discriminative training to construct speaker models for speaker verification and speaker identification. As opposed to conventional training which estimates a speaker´s model based only on the training utterances from the same speaker, we use a discriminative training approach which takes into account the models of other competing speakers and formulates the optimization criterion such that speaker recognition error rate on the training data is directly minimized. We also propose a normalized score function which makes the verification formulation consistent with the minimum error training objective. We show that the speaker recognition performance is significantly improved when discriminative training is incorporated