A speech signal based gender identification system using four classifiers

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), mu...

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Bibliographic Details
Main Authors: Djemili, R., Bourouba, H., Korba, M. C. A.
Format: Conference Proceeding
Language:English
Subjects:
Adaptation models
Gaussian mixture model (GMM)
Gender identification
MFCC
Multilayer Perceptrons (MLP)
Speech
Online Access:Request full text
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Summary: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.
DOI:10.1109/ICMCS.2012.6320122