A speech signal based gender identification system using four classifiers
Rafik Djemili, Hocine Bourouba, Mohamed Cherif Amara Korba · 2012
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.