DETAC: A discriminative criterion for speaker verification

This paper introduces a general criterion applicable to discriminative training of detection systems, and discusses its particular implementation in GMM-based text-independent speaker verification. Based on an analysis of the detection error trade-off curve of a baseline system, we argue that the new criterion extends several conventional methods such as the maximum posterior training by logistic regression and the linear discriminative analysis projection, by a second aspect - "reshaping" the Bayes error area in favor of a relevant operating range. Optimization results with relative error reduction of up to 16% are presented on the cellular task of the NIST-2001 speaker recognition evaluation.