Similarity normalization for speaker verification by fuzzy fusion

Similarity or likelihood normalization techniques are important for speaker veri"cation systems as they help to alleviate the variations in the speech signals. In the conventional normalization, the a priori probabilities of the cohort speakers are assumed to be equal. From this standpoint, we apply the theory of fuzzy measure and fuzzy integral to combine the likelihood values of the cohort speakers in which the assumption of equal a priori probabilities is relaxed. This approach replaces the conventional normalization term by the fuzzy integral which acts as a non-linear fusion of the similarity measures of an utterance assigned to the cohort speakers. We illustrate the performance of the proposed approach by testing the speaker veri"cation system with both the conventional and the fuzzy algorithms using the commercial speech corpus TI46. The results in terms of the equal error rates show that the speaker veri"cation system using the fuzzy integral is more #exible and more favorable than the conventional normalization method. ( 1999 Pattern Recognition Society. Published by Elsevier Science Ltd. All rights reserved.