Genetic learning of multi-attribute interactions in speaker verification

Author

Pham, Man

Author_Institution

Sch. of Comput., Univ. of Canberra, Canberra, ACT, Australia

Volume

1

fYear

2000

fDate

2000

Firstpage

379

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;

fLanguage

English

Publisher

ieee

Conference_Titel

Evolutionary Computation, 2000. Proceedings of the 2000 Congress on

Conference_Location

La Jolla, CA

Print_ISBN

0-7803-6375-2

Type

conf

DOI

10.1109/CEC.2000.870320

Filename

870320