A Fuzzy Approach to Speaker Verification

This paper proposes a fuzzy approach to speaker verification. For an input utterance and a claimed identity, most of the current methods compute a claimed speaker’s score, which is the ratio of the claimed speaker’s and the impostors’ likelihood functions, and compare this score with a given threshold to accept or reject this speaker. Considering the speaker verification problem based on fuzzy set theory, the claimed speaker’s score is viewed as the fuzzy membership function of the input utterance in the claimed speaker’s fuzzy set of utterances. Fuzzy entropy and fuzzy c-means membership functions are proposed as fuzzy membership scores, which are the ratios of functions of the claimed speaker’s and impostors’ likelihood functions. A likelihood transformation is also considered to relate current likelihood and fuzzy membership scores. We also proposed fuzzy scores using membership functions similar to those produced by noise-clustering-based method. This noise clustering concept provides very effective modifications to several methods, which can overcome some of the problems of ratio-type scores and greatly reduce the false acceptance rate. Experiments were performed to evaluate proposed normalization methods for speaker verification using the YOHO corpus. Experiments demonstrate that fuzzy methods and their noise clustering versions outperform conventional methods.