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This paper deals with the interaction between progressive model adaptation and score normalization strategies which are used for reducing the variation in likelihood ratio scores in making speaker verification decisions. This issue is important in establishing robust decision thresholds for practical speaker verification systems. An adaptive score normalization method is proposed that is designed to reduce the drift in likelihood ratio scores that occurs when speaker models are adapted. Thisdoi:10.1109/icassp.2008.4518745 dblp:conf/icassp/YinRK08 fatcat:waykjzudyreglh2w5yr7y2pvcu