PCM '02 Proceedings of the Third IEEE Pacific Rim Conference on Multimedia: Advances in Multimedia Information Processing
A multi-resolution multi-classifier system for speaker verification
Expert Systems: The Journal of Knowledge Engineering
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The cohort and world models are commonly used for scoring normalization in speaker verification. As these models represent different regions of the feature space, a better solution could be obtained by integrating them into a single framework. In this paper, we embed the two models in elliptical basis function networks and propose a two-stage decision procedure for improving verification performance. In the first stage, the score of an unknown utterance is normalized by a world model. If the difference between the resulting normalized score and a world threshold is sufficiently large, the claimant is accepted or rejected immediately. Otherwise, the score will be normalized by a cohort model and compared with a cohort threshold to make a final accept/reject decision. Experimental evaluations based on the YOHO corpus suggest that the two-stage method achieves a lower error rate as compared to the case where only one background model is used.