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Speech based Gender Identification using Feed Forward Neural Networks

Published on January 2016 by Seema Khanum, Marpe Sora
journal_cover_thumbnail

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
  1. Madhavi S. Pednekar, KavitaTiware and SachinBhagwat, "Gender Distinction Using Short Segments of Speech Signal", IJCSNS International Journal of Computer Science and Network Security, VOL. 8, No. 10, October 2008.
  2. Czyzewski, Andrzej; Dziubinski, Marek; Kotus, Jozef; Pawlik, Arkadiusz; Rypulak, Andrzej; Szwoch, Grzegorz, "Multitask Noisy Speech Enhancement System", 26th International Conference: Audio Forensics in the Digital Age (July 2005).
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  6. Mohammad Reza Homaeinezhad, EhsanTavakkoli, Ali Ghaffari, "Discrete Wavelet based Fuzzy Network Architecture for ECG Rhythm Type Recognition: Feature Extraction and Clustering Oriented Tuning of Fuzzy Inference System" International Journal of Signal Processing, Image Processing and Pattern Recognition, Vol. 4, No. 3, September, 2011.
  7. IEEE Subcommittee (1969). "IEEE Recommended Practice for Speech Quality Measurements. IEEE Trans. Audio and Electroacoustics", AU-17(3), 225-246.
  8. Hu, Y. and Loizou, P. (2007). "Subjective evaluation and comparison of speech enhancement algorithms," Speech Communication, 49, 588-601.
Index Terms

Computer Science
Information Sciences

Keywords

Gender Identification mfcc Ann Fuzzy C Mean Clustering