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Reseach Article

Pathological Voice Recognition for Vocal Fold Disease

by Pravena D, Dhivya S, Durga Devi A
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 47 - Number 13
Year of Publication: 2012
Authors: Pravena D, Dhivya S, Durga Devi A

Pravena D, Dhivya S, Durga Devi A . Pathological Voice Recognition for Vocal Fold Disease. International Journal of Computer Applications. 47, 13 ( June 2012), 31-37. DOI=10.5120/7250-0314

@article{ 10.5120/7250-0314,
author = { Pravena D, Dhivya S, Durga Devi A },
title = { Pathological Voice Recognition for Vocal Fold Disease },
journal = { International Journal of Computer Applications },
issue_date = { June 2012 },
volume = { 47 },
number = { 13 },
month = { June },
year = { 2012 },
issn = { 0975-8887 },
pages = { 31-37 },
numpages = {9},
url = { },
doi = { 10.5120/7250-0314 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
%0 Journal Article
%1 2024-02-06T20:42:52.511987+05:30
%A Pravena D
%A Dhivya S
%A Durga Devi A
%T Pathological Voice Recognition for Vocal Fold Disease
%J International Journal of Computer Applications
%@ 0975-8887
%V 47
%N 13
%P 31-37
%D 2012
%I Foundation of Computer Science (FCS), NY, USA

Pathology is the study and diagnosis of disease. Due to the nature of job, unhealthy habits and voice abuse, the people are subjected to the risk of voice problems. The diagnosis of vocal and voice disorders should be in the early stage otherwise it causes changes in the normal signal. It is well known that most of vocal fold pathologies cause changes in the acoustic voice signal. Therefore, the voice signal can be a useful tool to diagnose them. Acoustic voice analysis can be used to characterize the pathological voices. This paper presents the detection of vocal fold pathology with the aid of the speech signal recorded from the patients. We are going to recognize the disordered voice for vocal fold disease by focusing on the classification of pathological voice from healthy voice based on acoustic features. The method includes two steps. The first step is the extraction of feature vectors based on MFCC. The second is the classification of feature vectors using GMM. The extracted acoustic parameters from the voice signals are used as an input for the MFCC. The main advantage of this method is less computation time and possibility of real-time system development. This report introduces the design and implementation of the proposed system for recognizing pathological and normal voice. Also a description is given about the literature survey done and the implementation of different modules in the system. The result of the proposed system and the scope of improvements are also discussed in the report.

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Computer Science
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