A Pathological Voices Assessment Using Classification


  • T. Arikrishnan Department of Computer Science and Engineering, Anna University (BIT Campus), Trichy, Tamil Nadu, India
  • C. P. Darani Department of Computer Science and Engineering, Anna University (BIT Campus), Trichy, Tamil Nadu, India




Pathological voices, SVM


The diagnosing of pathological voice may be a tedious topic and it receives abundant attention. There are several diseases that adversely have an effect on our human speech (voice). The doctor will use only the equipments for detection of pathological voice. However, it is invasive and needs a skilled analysis of diverse human speech signal parameters. Automatic voice analysis for pathological speech has its own blessings, like 1)its quantitative and non-invasive nature. 2) permitting the identification and observance of vocal system diseases . Within the pathological voice classification techniques gathered by the voice of a patient, the goal is to discriminate whether the given voice is normal or pathological. From the speech Mel- Frequency Cepstral Coefficients (MFCC) has been extracted from the voice information and classified into two categories. However, the accuracy of the earlier classification methodology may need additional improvement. In my project work, Support Vector Machine (SVM) classifier is used for pathological voice classification with non-invasive nature to diagnose and analyze the voice of the patient.


R.A.Prosek, A. A. Montgemery, B. E. Walden, “An evaluation of residue features as correlates of voice disorders,” J. Commun. Disorders, Vol. 20, pp. 105-117, 1987.

E.Yumoto,“Harmonics-to-noise ratio as an Index of the degree of horseness,” J.Acoust.Soc.Am., Vol 71, pp.1544-1549,1989.

L.Eskenazi, D.G.Chinders, and D.M.Hicks,”Acoutics correlates of vocal quality”, J.Speech Hear.Res., Vol. 33,pp. 298-306, 1990.

F.Klingholz,”Acoustic recognition of voice disorders :A comparative study, running speech versus sustained vowels,” J. Acoust. Soc. Am., Vol.88, pp. 2218-2224, 1990.

L. Rabiner and B. H. Huang, Fundamentals of speech recognition. Englewood Cliffs, NJ: Prentice-Hall, 1993.

Y.Qi and R.E. Hillman, “Temporal and spectral estimations of harmonics to- noise ratio in human voice signals,” J.Acoust. Soc. Am., Vol. 102, No. 1, pp. 537-543,1997.

K.Umapathy, S.Krishnan, V.Parsa, and D.Jamieson, “Timefrequency modelling and classification of pathological voices,” in Proc. IEEE Engineering in Medicine and Biology Society (EMBS) 2002 Conference, Houston,TX,Oct.2002, pp. 116-117.

Y.D.Heman-Ackah et al., “Cepstral peak prominence: A more reliable measure of dysphonia,” Ann Otol., Rhinol., Laryngol., Vol. 112, No. 4, pp. 324-329, Apr. 2003.

Godino-Llorente and P.Gomez-Vilda, “Automatic detection of voice impairment by means of short-term cepstral parameters and neuralnetwork based detectors,” Vol.51, No.2, pp.380-384,Feb. 2004.

J. Nayak, P.S. Bhat, R. Acharya, U.V. Athal, “Classification and analysis of speech abnormatities”, ITBM – RBM, 26, pp. 319- 327.2005.




How to Cite

Arikrishnan, T., & Darani , C. P. (2014). A Pathological Voices Assessment Using Classification. Asian Journal of Computer Science and Technology, 3(1), 20–23. https://doi.org/10.51983/ajcst-2014.3.1.1730