CFP last date
20 May 2024
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.

  1. A. A. Dibazar, S. Narayanan, and T. W. Berger. Feature analysis for automatic detection of pathological speech. Proceedings of the Second Joint 24th Annual Conference and theAnnual Fall Meeting of the Biomedical Engineering Society] [Engineering in Medicine and Biology, 1:182–183, 2002.
  2. Carlo Drioli and Federico Avanzini. Hybrid parametricphysiological glottal modelling with application to voice quality assessment. Medical Engineering and Physics, 24(7- 8):453–460, 2002.
  3. L Gavidia-Ceballos and J H Hansen. Direct speech feature estimation using an iterative EM algorithm for vocal fold pathology detection. IEEE transactions on bio-medical engineering, 43(4):373–83, April 1996.
  4. A Gelzinis, A Verikas, and M Bacauskiene. Automated speech analysis applied to laryngeal disease categorization. Computer Methods and Programs in Biomedicine, 91(1):36– 47, July 2008.
  5. Juan Ignacio Godino-Llorente, Pedro G´omez-Vilda, and Manuel Blanco-Velasco. Dimensionality reduction of a pathological voice quality assessment system based on gaussian mixture models and short-term cepstral parameters. IEEE Transactions on Biomedical Engineering, 53(10):1943–1953, 2006.
  6. Stefan Hadjitodorov and Petar Mitev. A computer system for acoustic analysis of pathological voices and laryngeal diseases screening. Medical Engineering & Physics, 24(6):419– 429, July 2002.
  7. Zvi Kons, Aharon Satt, Ron Hoory, Virgilijus Uloza, Evaldas Vaiciukynas, Adas Gelzinis, and Marija Bacauskiene. On Feature Extraction for Voice Pathology Detection from Speech Signals. pages 3–6.
  8. Ian McLoughlin. Applied Speech and Audio Processing with MATLAB Examples. Cambridge University Press, 2009.
  9. Jagannath Nirmal, Pramod Kachare, Suprava Patnaik, and Mukesh Zaveri. Cepstrum liftering based voice conversion using RBF and GMM. In International Conference on Communication and Signal Processing, ICCSP 2013 - Proceedings, pages 570–575, 2013.
  10. Lc C Quilty, Km M Godfrey, and S H Kennedy. Harm avoidance as a mediator of treatment response to antidepressant treatment of patients with major depression. Psychotherapy and, 8:1–7, 2010.
  11. L. Rabiner and R. Schafer. Digital Processing of Speech Signals. Englewood Cliffs: Prentice Hall, 1978.
  12. R. T. Ritchings, M. McGillion, and C. J. Moore. Pathological voice quality assessment using artificial neural networks. Medical Engineering & Physics, 24(7-8):561–564, September 2002.
  13. V Sellam and J Jagadeesan. Classification of Normal and Pathological Voice Using SVM and RBFNN. Journal of Signal and Information Processing, 05(01):1–7, 2014.
  14. Virgilijus Uloza, Antanas Verikas, Marija Bacauskiene, Adas Gelzinis, Ruta Pribuisiene, Marius Kaseta, and Viktoras Saferis. Categorizing normal and pathological voices: Automated and perceptual categorization. Journal of Voice, 25(6):700–708, 2011.
  15. Xiang Wang, Jianping Zhang, and Yonghong Yan. Discrimination between pathological and normal voices using GMMSVM approach. Journal of Voice, 25(1):38–43, 2011.
Index Terms

Computer Science
Information Sciences


Pitch jitter lpc residual source excitation short time energy confusion matrix