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Reseach Article

Speech based Gender Identification using Feed Forward Neural Networks

Published on January 2016 by Seema Khanum, Marpe Sora
National Conference on Recent Trends in Information Technology
Foundation of Computer Science USA
NCIT2015 - Number 1
January 2016
Authors: Seema Khanum, Marpe Sora
e19691bb-e864-446a-ae8a-04c9c08ff7cc

Seema Khanum, Marpe Sora . Speech based Gender Identification using Feed Forward Neural Networks. National Conference on Recent Trends in Information Technology. NCIT2015, 1 (January 2016), 5-8.

@article{
author = { Seema Khanum, Marpe Sora },
title = { Speech based Gender Identification using Feed Forward Neural Networks },
journal = { National Conference on Recent Trends in Information Technology },
issue_date = { January 2016 },
volume = { NCIT2015 },
number = { 1 },
month = { January },
year = { 2016 },
issn = 0975-8887,
pages = { 5-8 },
numpages = 4,
url = { /proceedings/ncit2015/number1/23743-5192/ },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Proceeding Article
%1 National Conference on Recent Trends in Information Technology
%A Seema Khanum
%A Marpe Sora
%T Speech based Gender Identification using Feed Forward Neural Networks
%J National Conference on Recent Trends in Information Technology
%@ 0975-8887
%V NCIT2015
%N 1
%P 5-8
%D 2016
%I International Journal of Computer Applications
Abstract

This paper proposes an efficient method of gender identification based on the speaker's voice in a noisy environment. MFCC was used to extract features from the speech sample taken from a noisy speech database; these features are then used to train Artificial Neural Network architecture to classify two different genders (Male and Female). The test result shows that the new proposed ANN architecture can analyze and learn better and faster. The advantage of proposed method is a result of decreasing the number of segments by grouping similar segments in training data using a clustering technique namely fuzzy c means clustering.

References
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Index Terms

Computer Science
Information Sciences

Keywords

Gender Identification mfcc Ann Fuzzy C Mean Clustering