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Supervised neural network classifier for voice pathology

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Main Authors: Murugesa Pandiyan, Paulraj, Prof. Madya Dr,, Sazali, Yaacob, Prof. Dr., Sivanandam, S. N., Hariharan, Muthusamy
Other Authors: paul@unimap.edu.my
Format: Article
Language:English
Published: Kongu Engineering College 2011
Subjects:
Acoustic analysis
Feature extraction
Back propagation neural network
Slope parameter
Online Access:http://dspace.unimap.edu.my/xmlui/handle/123456789/14692
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spelling my.unimap-146922011-10-20T05:03:00Z Supervised neural network classifier for voice pathology Murugesa Pandiyan, Paulraj, Prof. Madya Dr, Sazali, Yaacob, Prof. Dr. Sivanandam, S. N. Hariharan, Muthusamy paul@unimap.edu.my Acoustic analysis Feature extraction Back propagation neural network Slope parameter Link to publisher's homepage at www.kongu.ac.in/ The classification of normal and pathological voices using noninvasive acoustical analysis features helps speech specialist to perform accurate diagnoses of vocal and voice disease. Acoustic analysis is a non-invasive technique based on digital processing of the speech signal. Ear Nose and throat (ENT)clinicians and speech therapists uses subjective techniques or invasive methods such as evaluation of voice quality by the specialist doctor's direct inspection and the observation of vocal folds by endoscopy techniques for the evaluation and diagnosis of voice pathologies. These Techniques provide inconvenience to the patient and depend on expertise of medical doctors. In the evaluation of quality speech, acoustic analyses of normal and pathological voices have become increasingly interesting to researcher in laryngology and speech pathologies. This paper present a new measure to parameterize the voice signal based on energy levels extracted from each frame of speech signal. A supervised neural net classifier for the classification of pathological voices using Back propagation with variable slope parameter is proposed. a simple scheme is proposed to fix the slope parameterof the bipolar/binar sigmoidal activation function. Simulation results indicate that the proposed classification algorithm distinguish the voice as pathological or a non-pathological voice accurately. 2011-10-20T05:03:00Z 2011-10-20T05:03:00Z 2008-01-03 Article p. 524 - 528 http://hdl.handle.net/123456789/14692 en Proceedings of the 2nd International Conference on Resource Utilization and Intelligent Systems 2008 Kongu Engineering College
institution Universiti Malaysia Perlis
building UniMAP Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Perlis
content_source UniMAP Library Digital Repository
url_provider http://dspace.unimap.edu.my/
language English
topic Acoustic analysis
Feature extraction
Back propagation neural network
Slope parameter
spellingShingle Acoustic analysis
Feature extraction
Back propagation neural network
Slope parameter
Murugesa Pandiyan, Paulraj, Prof. Madya Dr,
Sazali, Yaacob, Prof. Dr.
Sivanandam, S. N.
Hariharan, Muthusamy
Supervised neural network classifier for voice pathology
description Link to publisher's homepage at www.kongu.ac.in/
author2 paul@unimap.edu.my
author_facet paul@unimap.edu.my
Murugesa Pandiyan, Paulraj, Prof. Madya Dr,
Sazali, Yaacob, Prof. Dr.
Sivanandam, S. N.
Hariharan, Muthusamy
format Article
author Murugesa Pandiyan, Paulraj, Prof. Madya Dr,
Sazali, Yaacob, Prof. Dr.
Sivanandam, S. N.
Hariharan, Muthusamy
author_sort Murugesa Pandiyan, Paulraj, Prof. Madya Dr,
title Supervised neural network classifier for voice pathology
title_short Supervised neural network classifier for voice pathology
title_full Supervised neural network classifier for voice pathology
title_fullStr Supervised neural network classifier for voice pathology
title_full_unstemmed Supervised neural network classifier for voice pathology
title_sort supervised neural network classifier for voice pathology
publisher Kongu Engineering College
publishDate 2011
url http://dspace.unimap.edu.my/xmlui/handle/123456789/14692
_version_ 1643790615680385024
score 13.222552

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