Title :
Genetic learning of multi-attribute interactions in speaker verification
Author_Institution :
Sch. of Comput., Univ. of Canberra, Canberra, ACT, Australia
Abstract :
Genetic algorithms are applied to identify the interactions of multiple speech features, represented by fuzzy measures, for speaker recognition. This work aims to investigate more thoroughly the use of fuzzy measures and fuzzy integral in information fusion by means of genetic optimization. The proposed approach is implemented into the speaker verification system and tested against a commercial speech corpus. The results in terms of equal error rates show that the proposed speaker verification system is more favorable than the conventional normalization, and λ-measure fuzzy-integral based methods
Keywords :
fuzzy logic; genetic algorithms; speaker recognition; fuzzy integral; fuzzy measures; genetic algorithms; genetic learning; information fusion; lambda measure fuzzy-integral based methods; multi-attribute interactions; multiple speech features; normalization; speaker recognition; speaker verification; speech corpus; Australia; Data security; Error analysis; Fuzzy sets; Fuzzy systems; Genetic algorithms; Speaker recognition; Speech; System testing; Telecommunications;
Conference_Titel :
Evolutionary Computation, 2000. Proceedings of the 2000 Congress on
Conference_Location :
La Jolla, CA
Print_ISBN :
0-7803-6375-2
DOI :
10.1109/CEC.2000.870320
