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Detecting multiple system atrophy, Parkinson and other neurological disorders using voice analysis

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Abstract

In this study, we wanted to discriminate between three groups of patients. Each group contains nine voice recordings collected from nine patients. The three groups are: Patients who suffer From Multiple system atrophy, patients who suffer from Parkinson’s disease and patients who suffer from other neurological disorders. We extracted from each voice sample from 1 to 20 cepstral coefficients of the MFCC, PLP and RASTA-PLP. We used for classification Support Vector Machines with its different kernels. The results were very encouraging and they varied between 92.59 and 100% of accuracy.

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Authors and Affiliations

  1. Laboratoire de Recherche en Génie Electrique, Ecole Normale Supérieure de l’Enseignement Technique, Mohammed V University, Rabat, Morocco

    Achraf Benba, Abdelilah Jilbab & Ahmed Hammouch

Authors
  1. Achraf Benba
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  2. Abdelilah Jilbab
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  3. Ahmed Hammouch
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Corresponding author

Correspondence to Achraf Benba.

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All authors declares that they has no conflict of interest.

Ethical Approval

All procedures followed were in accordance with the ethical standards of the responsible committee on human experimentation (institutional and national) and with the Helsinki Declaration of 1975, as revised in 2008 (5).

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Benba, A., Jilbab, A. & Hammouch, A. Detecting multiple system atrophy, Parkinson and other neurological disorders using voice analysis. Int J Speech Technol 20, 281–288 (2017). https://doi.org/10.1007/s10772-017-9404-6

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  • DOI: https://doi.org/10.1007/s10772-017-9404-6

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