Minimization of Utterance Verification Error Rate as a Constrained Optimization Problem

Abstract

Abstract-Since utterance verification (UV) may be treated as a 2-class classification problem, it may be improved with discriminative training such as minimum verification error training or minimum verification error rate training. However, since in practice, one usually has to pick a specific false-acceptance or false-rejection rate for one's system, it is more desirable to optimize UV performance at a particular operating point. In this paper, we show that further improvement can be achieved by treating UV at a specific operating point as a constrained optimization problem.Index Terms-utterance verification, minimum verification error, minimum verification error rate

Improving the characterization of the alternative hypothesis via minimum verification error training with applications to speaker verification

“…Although MVE training has been extensively studied in the literature [14][15][16][17][18][19][20], most studies focus on better estimating the parameters of the target model. In contrast, we try to improve the characterization of the alternative hypothesis by applying MVE training to optimize the parameters associated with the combinations of the likelihoods from a set of background models.…”