Voice Analysis for Detecting Persons with Parkinson’s Disease using MFCC and VQ

  • Benba A
  • Jilbab A
  • Hammouch A
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Abstract

In order to improve the assessment of speech disorders in the context of Parkinson's disease, we have collected 34 sustained vowel / a /, from 34 subjects including 17 Parkinsonian patients. We subsequently extracted from 1 to 20 coefficients of the Mel-Frequency Cepstral Coefficients (MFCCs) from each subject. The frames were compressed using vector quantization, with six Codebook sizes. We used a leave-one-subject-out (LOSO) validation scheme and the Support Vector Machines (SVM) classifier with its different types of kernels, (i.e.; RBF, Linear and polynomial). After viewing the obtained results, we proceeded to a bench of 100 trials. The best average result obtained was 82% using the codebook size of 1.

Author-supplied keywords

  • loso
  • mfccs
  • parkinson
  • quantization
  • s disease
  • svm
  • vector
  • voice analysis

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Authors

  • A Benba

  • A Jilbab

  • A Hammouch

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