Identification Of Parkinson’S Disease Using Acousticanalysis Of Poem Recitation
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Parkinson’s disease (PD) is the second most frequent neurodegenerative disorder. It is estimated that 60–90% of PD patients suffer from speech disorder called hypokinetic dysarthria (HD). The goal of this work is to reveal influence of poem recitation on acoustic analysis of speech and propose concept of Parkinson’s disease identification based on this analysis. Classification methods used in this work are Random Forests and Support Vector Machine. The best achieved accuracy of disease identification is 70.66% with 59.25% sensitivity for Random Forests classifier fed mainly with articulation features. These results demonstrate a high potential of research in this area.
Keywordspoem recitation, acoustic analysis, binary classification, Parkinson’s disease, hypokinetic dysarthria
Document typePeer reviewed
Document versionxmlui.vut.verze.Publishers's version
SourceProceedings of the 23st Conference STUDENT EEICT 2017. s. 619-623. ISBN 978-80-214-5496-5
- Student EEICT 2017