Parkinson’s Disease Recognition by Speech Acoustic Parameters Classification

  • D. MeghraouiEmail author
  • B. Boudraa
  • T. Merazi-Meksen
  • M. Boudraa
Conference paper
Part of the Lecture Notes in Networks and Systems book series (LNNS, volume 1)


Thanks to improvement of means of communication performance and intelligent systems, research works to detect speech disorders by analysing voice signals are very promising. This paper demonstrates that dysarthria in people with Parkinson’s disease (PWP) can be diagnosed using a classification of the characteristics of their voices. For this purpose, we have experimented two types of classifiers, namely Bernoulli and multinomial naïve Bayes in order to select the most pertinent features parameters for diagnosing PWP. The prediction accuracy achieved by using multinomial naive Bayes (NB) classifier model reaching 95 % is very encouraging.


Speech analysis Parkinson’s disease recognition Naïve Bayes Bernoulli naïve Bayes Multinomial naïve Bayes 



We thank Mr Benba Achraf, from the university Mohamed five, Rabat, Morocco, and Miss Hadjaj Hassina from the university of Science and Technology Houari Boumediene, Algiers, Algeria for helpful discussions.


  1. 1.
    Ishihara, L., Brayne, C.: A systematic review of depression and mental illness preceding Parkinson’s disease. Acta Neurologica Scandinavica 211–220 (2005)Google Scholar
  2. 2.
    Little, M.A. et al.:Suitability of dysphonia measurements for telemonitoring of Parkinson’s disease. IEEE Trans. Biomed. Eng. 1015–1022 (2009)Google Scholar
  3. 3.
    Hernandez-Espinosa, C., et al.: Diagnosis of vocal and voice disorders by the speech signal. In: Proceedings of the IEEE-INNS-ENNS International Joint Conference, Italy, pp. 253–258 (2000)Google Scholar
  4. 4.
    O’Sullivan, S.B., Schmitz, T.J.: Parkinson disease. In: Physical Rehabilitation, 5th edn, pp. 856–894. F. A. Davis Company, Philadelphia, PA, USA (2007)Google Scholar
  5. 5.
    Kent, R.D.: Hearing and believing: some limits to the auditory-perceptual assessment of speech and voice disorders. Am. J. Speech Lang. Pathol. 5(3), 7–23 (1996)Google Scholar
  6. 6.
    Rahn, D.A., Chou, M., et al.: Phonatory impairment in Parkinson’s disease: evidence from nonlinear dynamic analysis and perturbation analysis. J. Voice 21(1), 64–71 (2005)Google Scholar
  7. 7.
    Sakar, B.E., et al.: Collection and analysis of a Parkinson speech dataset with multiple types of sound recordings. IEEE J. Biomed. Health Inf. 828–834 (2013)Google Scholar
  8. 8.
    Styler, W.: Using praat for linguistic research, University of Coloradoat Boulder Phonetics Lab, Last Update: March (2014)Google Scholar
  9. 9.
    Farrús, M., et al.: Jitter and Shimmer measurements for speaker recognition. In: Proceedings of International Conference Interspeech, pp.778–781 (2007)Google Scholar
  10. 10.
    ArefiShirvan, R., Tahami, E.: Voice analysis for detecting Parkinson’s disease using genetic algorithm and KNN classification method. Biomed. Eng. 14–16 (2011)Google Scholar
  11. 11.
    Hart, J.T., Collier, R., Cohen, A.: A Perceptual Study of Intonation. Cambridge University Press (2006)Google Scholar
  12. 12.
    Yongjian, F.: Data mining: tasks, techniques and applications. IEEE Potentials 16, 18–20 (1997)Google Scholar
  13. 13.
    Mary, L., Yegnanarayana, B.: Extraction and representation of prosodic features for language and speaker recognition. Speech Commun. 50(10), 782–796 (2008). ElsevierGoogle Scholar
  14. 14.
    Bayes, T.: An essay towards solving a problem in the doctrine of chances. Philos. Trans. R. Soc. Publ. 53 370–418 (2012)Google Scholar
  15. 15.
    Manning, C., et al.: Introduction to Information Retrieval. Cambridge University Press, USA, April (2009)Google Scholar
  16. 16.
    Metsis, V., et al.: Spam filtering with Naive Bayes—Which Naive Bayes? In: 3rd Conference on Email and Anti-Spam (CEAS), July (2006)Google Scholar
  17. 17.
    Wang, Z., Bovik, A.: Mean squared error: love it or leave it? IEEE Signal Process. Mag. 98–117 (2009)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • D. Meghraoui
    • 1
    Email author
  • B. Boudraa
    • 1
  • T. Merazi-Meksen
    • 1
  • M. Boudraa
    • 1
  1. 1.Faculty of Electronics and InformaticsUniversity of Science and Technology Houari Boumediene USTHBBab Ezzouar, AlgiersAlgeria

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