A Hybrid Approach to Gender Classification using Speech Signal

Authors(2) :-M. Yasin Pir, Mohamad Idris Wani

Speech forms a significant means of communication and the variation in pitch of a speech signal of a gender is commonly used to classify gender as male or female. In this study, we propose a system for gender classification from speech by combining hybrid model of 1-D Stationary Wavelet Transform (SWT) and artificial neural network. Features such as power spectral density, frequency, and amplitude of human voice samples were used to classify the gender. We use Daubechies wavelet transform at different levels for decomposition and reconstruction of the signal. The reconstructed signal is fed to artificial neural network using feed forward network for classification of gender. This study uses 400 voice samples of both the genders from Michigan University database which has been sampled at 16000 Hz. The experimental results show that the proposed method has more than 94% classification efficiency for both training and testing datasets.

Authors and Affiliations

M. Yasin Pir
Department of Computer Applications, Govt. Degree College, Pattan, Baramulla, Jammu and Kashmir, India
Mohamad Idris Wani
Department of Electrical Engineering, Jamia Millia Islamia, New Delhi, India

Wavelet, Neural Networks, Stationary Wavelet Transform, Sampling, Power Spectral Density and Gender Classification.

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Publication Details

Published in : Volume 6 | Issue 1 | January-February 2019
Date of Publication : 2019-01-30
License:  This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 17-24
Manuscript Number : IJSRSET196110
Publisher : Technoscience Academy

Print ISSN : 2395-1990, Online ISSN : 2394-4099

Cite This Article :

M. Yasin Pir, Mohamad Idris Wani, " A Hybrid Approach to Gender Classification using Speech Signal, International Journal of Scientific Research in Science, Engineering and Technology(IJSRSET), Print ISSN : 2395-1990, Online ISSN : 2394-4099, Volume 6, Issue 1, pp.17-24, January-February-2019. Available at doi : https://doi.org/10.32628/IJSRSET196110
Journal URL : http://ijsrset.com/IJSRSET196110

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