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Reseach Article

Vocal Features for Glottal Pathology Detection using BPNN

by Ashwini Visave, Pramod Kachare, Amutha Jeyakumar, Alice Cheeran, Gurmit Bachher
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 118 - Number 17
Year of Publication: 2015
Authors: Ashwini Visave, Pramod Kachare, Amutha Jeyakumar, Alice Cheeran, Gurmit Bachher

Ashwini Visave, Pramod Kachare, Amutha Jeyakumar, Alice Cheeran, Gurmit Bachher . Vocal Features for Glottal Pathology Detection using BPNN. International Journal of Computer Applications. 118, 17 ( May 2015), 1-6. DOI=10.5120/20834-3571

@article{ 10.5120/20834-3571,
author = { Ashwini Visave, Pramod Kachare, Amutha Jeyakumar, Alice Cheeran, Gurmit Bachher },
title = { Vocal Features for Glottal Pathology Detection using BPNN },
journal = { International Journal of Computer Applications },
issue_date = { May 2015 },
volume = { 118 },
number = { 17 },
month = { May },
year = { 2015 },
issn = { 0975-8887 },
pages = { 1-6 },
numpages = {9},
url = { },
doi = { 10.5120/20834-3571 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
%0 Journal Article
%1 2024-02-06T23:01:56.392757+05:30
%A Ashwini Visave
%A Pramod Kachare
%A Amutha Jeyakumar
%A Alice Cheeran
%A Gurmit Bachher
%T Vocal Features for Glottal Pathology Detection using BPNN
%J International Journal of Computer Applications
%@ 0975-8887
%V 118
%N 17
%P 1-6
%D 2015
%I Foundation of Computer Science (FCS), NY, USA

Development of low cost, non-invasive applications is one of the most challenging tasks in the field of biomedical signal processing. Present work focuses on detection of glottal pathology with the knowledge of prominent speech processing and machine learning techniques. This paper addresses the discriminative characteristics of speech signal like, pitch, jitter, linear prediction residual and cepstral source excitation to aid such an identification system. Back-propagation Neural Network model is developed for various feature combinations to classify the glottal pathologic voice from normal voice. Accuracy of the developed system is evaluated considering different feature sets. Work also concludes the efficiency of such acoustic features for various combinations using objective measures like confusion matrix, true positive rate i. e. sensitivity, specificity i. e. true negative rate and accuracy. The results show promising development in identification of pathological individual from normal person using voice samples.

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Index Terms

Computer Science
Information Sciences


Pitch jitter lpc residual source excitation short time energy confusion matrix