CFP last date
20 December 2024
Call for Paper
January Edition
IJCA solicits high quality original research papers for the upcoming January edition of the journal. The last date of research paper submission is 20 December 2024

Submit your paper
Know more
The week's pick
Responsive image
Improved Shuffled Frog Leaping Algorithm with Self-Adaptive Shuffling for Fuzzy Logic PD+G Controller Optimization in Robotic Manipulators

Duc Hoang Nguyen
Random Articles
Reseach Article

Classification of Normal and Pathological Voice using GA and SVM

by V. Srinivasan, V. Ramalingam, V. Sellam
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 60 - Number 3
Year of Publication: 2012
Authors: V. Srinivasan, V. Ramalingam, V. Sellam
10.5120/9675-4102

V. Srinivasan, V. Ramalingam, V. Sellam . Classification of Normal and Pathological Voice using GA and SVM. International Journal of Computer Applications. 60, 3 ( December 2012), 34-39. DOI=10.5120/9675-4102

@article{ 10.5120/9675-4102,
author = { V. Srinivasan, V. Ramalingam, V. Sellam },
title = { Classification of Normal and Pathological Voice using GA and SVM },
journal = { International Journal of Computer Applications },
issue_date = { December 2012 },
volume = { 60 },
number = { 3 },
month = { December },
year = { 2012 },
issn = { 0975-8887 },
pages = { 34-39 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume60/number3/9675-4102/ },
doi = { 10.5120/9675-4102 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:05:41.075624+05:30
%A V. Srinivasan
%A V. Ramalingam
%A V. Sellam
%T Classification of Normal and Pathological Voice using GA and SVM
%J International Journal of Computer Applications
%@ 0975-8887
%V 60
%N 3
%P 34-39
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
Abstract

The analysis of pathological voice is a challenging and an important area of research in speech processing. Acoustic characteristics of voice are used mainly to discriminate normal voices from pathological voices. This study explores methods to find the ability of acoustic parameters in discrimination of normal voices from pathological voices. An attempt is made to analyze and to classify pathological voice from normal voice in children. The classification of pathological voice from normal voice is implemented using support vector machine (SVM). The normal and pathological voices of children are used to train and test the classifier. A dataset is constructed by recording speech utterances of a set of Tamil phrases. The speech signal is then analyzed in order to extract the acoustic parameters such as the Signal Energy, pitch, formant frequencies, Mean Square Residual signal, Reflection coefficients, Jitter and Shimmer. In this study various acoustic features are combined to form a feature set, so as to detect voice disorders in children based on which further treatments can be prescribed by a pathologist. A Genetic Algorithm (GA) based feature selection is utilized to select best set of features which improves the classification accuracy.

References
  1. L. Salhi, M. Talbi, A. Cherif "Voice Disorders Identification Using Hybrid Approach: Wavelet Analysis and Multilayer Neural Networks", World Academy of Science, Engineering and Technology 2008.
  2. Awrence R. Rabiner,"On the Use of Autocorrelation Analysis for Pitch Detection", IEEE Transactions on Acoustics, Speech, and Signal Processing, Vol ASSP-25, No. 1, Feb 1977.
  3. Pend Ce, Xu Qiujing, Wan Baikun, Chen Wenxi," Pathological Voice Classification Based on Features Dimension Optimization", Transactions of Tianjin University,Vol. 13,No. 6,Dec 2007.
  4. P. Dhanalakshmi*,S. Palanivel,V. Ramalingam,"Classification of Audio Signals Using SVM and RBFNN", Expert Systems with Applications 36(2009) 6069-6075 .
  5. Evaldas Vaiciukynas, Adas Gelzins, Marija Bacauskiene, Antanas Verikas,Aurelija Vegiene," Exploring Kernels in SVM-Based Classification of Larynx Pathology from Human Voice",Department of Electrical and Control Instrumentation, Kaunas University of Technology, Lithuania.
  6. Pravena D, Dhivya S,Durga DeviA,"Pathological Voice Recognition for Vocal Fold Didease", International Journal of Computer Applications(0975-888), Volume 47-No. 13,June 2012
  7. Marcelo de Oliviera Rosa*,Jose Carlos Pereira, and Marcos Grellet," Adaptive Estimation of Residue Signal for Voice Pathology Diagnosis" IEEE Transactions on Biomedical Engineering,Vol. 47, No. 1,Jan 2000.
  8. Mireia Farra, Javier Hernando, Pascual Ejarque," Jitter and Shimmer Measurements for Speaker Recognition", TALP Research Center, Department of Signal Theory and Communicati0ons, Universitat Politecnica de Catalunya, Barcelona,Spain.
  9. L. R. Rabiner, R. W. Schafer,"Digital Processing of Speech Signals"
  10. Yoav Meden, Eyal Yair, and Dan Chazan," Super Resolution Pitch Determination of Speech Signals", IEEE transactions on Signal Processing,Vol. 39,No. 1,Jan 1991.
  11. Ahmed Al-Ani," Ant Colony Optimization for Feature Subset Selection", Proceedings of World Academy of Science, Engineering and Technology, Feb 2005, Vol. 4 ISSN1307-6884.
  12. O. Ludwig, U. Nunes," Novel Maximum Margin Training algorithms for Supervised Neural Networks", IEEE Transactions on Neural Networks,vol. 21,issue 6,pp. 972-984,Jun. 2010.
  13. Z. Sun, X. Yuan, G. Bebis, S. Louis," Neural Network Based Gender Classification Using Genetic Eigen Feature Extraction", IEEE International Joint Conference on Neural Networks, May 2002.
Index Terms

Computer Science
Information Sciences

Keywords

Pitch Formants Jitter Shimmer Signal Energy Reflection Coefficients Genetic Algorithm SVM

Powered by PhDFocusTM