A speech signal based gender identification system using four classifiers

Author

Djemili, Rafik ; Bourouba, Rocine ; Korba, Mohamed Cherif Amara

Author_Institution

Electr. Eng. Dept., Univ. du 20 Aout 1955, Skikda, Algeria

fYear

2012

fDate

10-12 May 2012

Firstpage

184

Lastpage

187

Abstract

This paper presents a study of four different classifiers in the task of automatic speech based gender identification. Gender identification could have several applications in automatic speech and speaker recognition systems and in content -based multimedia indexing. Gaussian mixture model (GMM), multilayer perceptrons (MLP), vector quantization (VQ) and learning vector quantization (LVQ) are the classifiers used in this work along with mel frequency cepstral coefficients (MFCC). The performance attained by our best system is 96.4% identification accuracy using only 1s of speech per speaker using the IViE corpus.

Keywords

Gaussian processes; gender issues; learning (artificial intelligence); multilayer perceptrons; pattern classification; speech recognition; vector quantisation; GMM classifier; Gaussian mixture model classifier; IViE corpus; LVQ classifier; MFCC; MLP classifier; VQ classifier; automatic speech signal-based gender identification system; identification accuracy; learning vector quantization classifier; mel frequency cepstral coefficients; multilayer perceptron classifier; vector quantization classifier; Adaptation models; Speech; Gaussian mixture model (GMM); Gender identification; MFCC; Multilayer Perceptrons (MLP);

fLanguage

English

Publisher

ieee

Conference_Titel

Multimedia Computing and Systems (ICMCS), 2012 International Conference on

Conference_Location

Tangier

Print_ISBN

978-1-4673-1518-0

Type

conf

DOI

10.1109/ICMCS.2012.6320122

Filename

6320122