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International Journal of Speech Technology
Published in: International Journal of Speech Technology 4/2016

03-09-2016

Voice assessments for detecting patients with Parkinson’s diseases using PCA and NPCA

Authors: Achraf Benba, Abdelilah Jilbab, Ahmed Hammouch

Published in: International Journal of Speech Technology | Issue 4/2016

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Abstract

In this study, we wanted to discriminate between two groups of people. The database used in this study contains 20 patients with Parkinson’s disease and 20 healthy people. Three types of sustained vowels (/a/, /o/ and /u/) were recorded from each participant and then the analyses were done on these voice samples. Firstly, an initial feature vector extracted from time, frequency and cepstral domains. Then we used linear and nonlinear feature extraction techniques, principal component analysis (PCA), and nonlinear PCA. These techniques reduce the number of parameters and choose the most effective acoustic features used for classification. Support vector machine with its different kernel was used for classification. We obtained an accuracy up to 87.50 % for discrimination between PD patients and healthy people.
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Metadata
Title
Voice assessments for detecting patients with Parkinson’s diseases using PCA and NPCA
Authors
Achraf Benba
Abdelilah Jilbab
Ahmed Hammouch
Publication date
03-09-2016
Publisher
Springer US
Published in
International Journal of Speech Technology / Issue 4/2016
Print ISSN: 1381-2416
Electronic ISSN: 1572-8110
DOI
https://doi.org/10.1007/s10772-016-9367-z

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