Priori threshold determination for phrase-prompted speaker verification

This paper presents a novel method to determine the decision thresholds of speaker verification systems using enrollment data only. In the method, a speaker model is trained to differentiate the voice of the corresponding speaker and that of a general population. This is accomplished by using the speaker's utterances and those of some other speakers (denoted as anti-speakers) as the training set. Then, an operation environment is simulated by presenting the utterances of some pseudo-impostors (none of them is an anti-speaker) to the speaker model The threshold is adjusted until the chance of falsely accepting a pseudo-impostor falls below an application dependent level. Experimental evaluations based on 138 speakers of the YOHO corpus suggest that with a simulated operation environment, it is able to determine the best compromise between false acceptance and false rejection.