Fuzzy Normalisation Methods for Pattern Verification
نویسنده
چکیده
A fuzzy approach to normalization methods for pattern verification is presented in this paper. For an input object and a claimed identity, a claimed pattern's score is calculated and compared with a given threshold to accept or reject the claimed pattern. Considering the pattern verification problem based on fuzzy set theory, the claimed pattern's score is viewed as a fuzzy membership function. Fuzzy entropy and fuzzy c-means membership functions are proposed as fuzzy membership scores. A likelihood transformation is also considered to propose more fuzzy membership scores. Furthermore, the noise clustering method supplies a very effective modification to all methods, which can overcome some of the problems of ratio-type scores and reduce the false acceptance rate. Experiments were performed to evaluate proposed normalization methods for speaker verification using the ANDOSL database and utterance verification using the TI46 database. Experiments showed better results for fuzzy normalization methods.
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Fuzzy normalisation methods for speaker verification
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