Fuzzy-integration based normalization for speaker verification

Similarity normalization techniques are important for speaker verification systems as they help to better cope with speaker variability. 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 cohort speakers. Experimental results show that the speaker verification system using the fuzzy integral is more flexible and favorable than the conventional method.