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Design of System for Classification of Vocal Cord/Glottis Carcinoma using ANN and Support Vector Machine

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2015
Syed Mohammad Ali, Pradeep Tulshiram Karule

Syed Mohammad Ali and Pradeep Tulshiram Karule. Article: Design of System for Classification of Vocal Cord/Glottis Carcinoma using ANN and Support Vector Machine. International Journal of Computer Applications 132(4):1-7, December 2015. Published by Foundation of Computer Science (FCS), NY, USA. BibTeX

	author = {Syed Mohammad Ali and Pradeep Tulshiram Karule},
	title = {Article: Design of System for Classification of Vocal Cord/Glottis Carcinoma using ANN and Support Vector Machine},
	journal = {International Journal of Computer Applications},
	year = {2015},
	volume = {132},
	number = {4},
	pages = {1-7},
	month = {December},
	note = {Published by Foundation of Computer Science (FCS), NY, USA}


Decision support system in voice disorder classification has developed more and more momentum now days because of complication in routine methods. Neurological disorder creates speech problems. Therefore, decision support system can serve as an important mean to detect voice disorders.

In this research work, normal & vocal cord cancer voice samples are used & a system is designed to classify vocal cord cancer speech from Normal speech. Vocal cord carcinoma is defined as a malignant tumor in the vocal fold. It is a form of laryngeal cancer, also called as glottis cancer. Pre-processed diseased and normal speech signals are used for spectral analysis to detect disease. Autocorrelation of speech signals is calculated to see the difference between normal and vocal cord cancer speech signal. Two sets of twenty five features are calculated and three neural networks like MLP, GFF, Modular and SVM are used for classification. Feature sets, Networks with highest classification accuracy were found. It is observed that the accuracy of this disease classification is 100%.


  1. Salhi, L., Mourad, T., Cherif, A.,2010 “Voice Disorders Identification Using Multilayer Neural Network”, The International Arab Journal of Information Technology, Volume 7-No.2, (April 2010),177-185.
  2. Hariharan, M., Paulraj, M.P., Jaacob, S., 2010, “Time Domain Features and Probabilistic Neural Network For the Detection Of Vocal Fold Pathology”, Malaysian journal Of Computer Science, Vol(23) (2010),60-67.
  3. Putzer, M., Koreman, J., 1997, “A german database for a pattern for vacal fold vibration” Phonus 3, Institute of Phonetics, University of the Saarland, Tavel, P. 2007 Modeling and Simulation Design. AK Peters Ltd, 143-153.
  4. Proakis, J. G. G., Manolakis, “Digital Signal Processing. Principles, Algorithm and Applications”, Prentice Hall India, Third Eition.309, 122.
  5. Orzechowski, Izworski,A., Izworski, R., Tadeusiewiez ,K., Chmunzynska, P., Radkowski, I., Gotkowska, 2005, “ Processing of pathological change in speech caused by Dysarthria ”, IEEE Proceedings of 2005 International symposium on intelligent signal processing & communication system, 49-52.
  6. Oliveira Rosa, M. de, Pereira ,J. C, Gellet, M.,2000 “Adaptive Estimation of Residue Signal for Voice Pathology Diagnose”, IEEE Transaction on Biomedical Engineering , Vol.47, No.1(Jan.2000).
  7. Cesar , M. E., Hugo, R. L.,2000, “Acoustic Analysis of speech for detection of Laryngeal pathologies”, IEEE proceeding of the 22nd Annual EMBS international conference chicago IL,(2000) ,2369-2372.
  8. Picone, J.W., 1993 “Signal modeling techniques in speech recognition”, Proceedings of the IEEE, Vol.81, No.9, Sept.1993.
  9. Lipeika, A., Lipeikiene, J., Lipeikiene, L., Telksnys,2000, “Development of Isolated speech Recognition System”, INFORMATICA, Vol 13, No.1, 2002,37-46.
  10. Sigmund, M. “Voice Recognition By Computer”, TectumVerlag publication, pp no20-22.
  11. Gill, M. K.,2010 “Vector Quantization based Speaker identification”, International Journal of Computer Application(0975-8887)Volume 4-No.2,2010,1-4.
  12. Schafer ,R.W., Rabiner, L.R.,1970 “System for automatic formant analysis of voiced speech,”J.Amer.,vol.47, (Feb.1970) 634-648,
  14. Fernandes, M., Mattioli, F.E.R., LamounierJr.,E.A. and Andrade,A.O.,2011,“Assesment of Laryngeal Disorders Through TheGlobal Energy of Speech,”IEEE Latin American Transactions,vol.9,No.7,( December 2011).
  15. Rabiner,L. R.,1977 “On the use of Autocorrelation Analysis for Pitch Detection”,IEEE Transaction Acoustics,Speech and signal Processing ”,Vol.ASSP-25,No.1, (February 1977),24-30.


Vocal cord speech signals; Spectral analysis; Feature extraction, SVM; MLP; Feed forward; Modular Networks.