A connectionist approach for automatic speaker identification

Author

Bennani, Younés ; Soulie, Françoise Fogelman ; Gallinari, Patrick

Author_Institution

Lab. de Recherche en Inf., Univ. de Paris Sud, Orsay, France

fYear

1990

fDate

3-6 Apr 1990

Firstpage

265

Abstract

A connectionist approach to automatic speaker identification based on the learning vector quantization (VQ) algorithm is presented. For each adherent to the identification system, a number of references is fixed. The algorithm is based on a nearest-neighbor principle, with adaptation through learning. The identification is realized by comparing to a given threshold the distance of the unknown utterance to the nearest reference. Preliminary tests run on a ten-speaker set show an identification rate of 97% for MFC coefficients. The identification system and database used and the results obtained for different combinations of parameters are given. The system is evaluated by comparing its performances with a Bayesian system

Keywords

learning systems; speech recognition; Bayesian system; MFC coefficients; automatic speaker identification; connectionist approach; identification rate; learning vector quantization; nearest-neighbor principle; unknown utterance; Bayesian methods; Buildings; Databases; Linear predictive coding; Mel frequency cepstral coefficient; Microphones; Nearest neighbor searches; Performance evaluation; Speech; System testing; Testing; Vector quantization;

fLanguage

English

Publisher

ieee

Conference_Titel

Acoustics, Speech, and Signal Processing, 1990. ICASSP-90., 1990 International Conference on

Conference_Location

Albuquerque, NM

ISSN

1520-6149

Type

conf

DOI

10.1109/ICASSP.1990.115619

Filename

115619