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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)

Abstract

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.

Keywords

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

Notes

Acknowledgment

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.

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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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