A two-stage scoring method combining world and cohort models for speaker verification
W.D. Zhang, Man‐Wai Mak, Xiaohai He · 2002
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.