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Glottal Excitation Feature based Gender Identification System using Ergodic HMM

by R. Rajeshwara Rao, A. Prasad
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 17 - Number 3
Year of Publication: 2011
Authors: R. Rajeshwara Rao, A. Prasad
10.5120/2200-2794

R. Rajeshwara Rao, A. Prasad . Glottal Excitation Feature based Gender Identification System using Ergodic HMM. International Journal of Computer Applications. 17, 3 ( March 2011), 31-36. DOI=10.5120/2200-2794

@article{ 10.5120/2200-2794,
author = { R. Rajeshwara Rao, A. Prasad },
title = { Glottal Excitation Feature based Gender Identification System using Ergodic HMM },
journal = { International Journal of Computer Applications },
issue_date = { March 2011 },
volume = { 17 },
number = { 3 },
month = { March },
year = { 2011 },
issn = { 0975-8887 },
pages = { 31-36 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume17/number3/2200-2794/ },
doi = { 10.5120/2200-2794 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:04:40.769101+05:30
%A R. Rajeshwara Rao
%A A. Prasad
%T Glottal Excitation Feature based Gender Identification System using Ergodic HMM
%J International Journal of Computer Applications
%@ 0975-8887
%V 17
%N 3
%P 31-36
%D 2011
%I Foundation of Computer Science (FCS), NY, USA
Abstract

In this paper, through different experimental studies it is demonstrated that the time varying glottal excitation component of speech can be exploited for text independent gender recognition studies. Linear prediction (LP) residual is used as a representation of excitation information in speech. The gender-specific information in the excitation of voiced speech is captured using the Hidden Markov Models (HMMs). The decrease in the error during training and recognizing genders during testing phase close to 100 % accuracy demonstrates that the excitation component of speech contains gender-specific information and is indeed being effectively captured by continuous Ergodic HMM. A gender recognition study using gender specific features for different HMM states, mixture components, size of testing data on the performance of the gender recognition is evaluated. We demonstrate the gender recognition studies on TIMIT database.

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Index Terms

Computer Science
Information Sciences

Keywords

Gender Hidden Markov Model (HMM) LPC MFCC

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