Potential of Prosodic Features to Estimate Degree of Parkinson's Disease Severity
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This paper deals with non-invasive and objective Parkinson’s disease (PD) severity estimation. For this purpose, prosodic speech features expressing monopitch, monoloudness, and speech rate abnormalities were extracted from recordings of stress-modified reading task acquired from 72 patients with idiopathic PD. Using a single feature regression (esimating values of subjective clinical rating scales) with classification and regression algorithm, following performance in terms of root mean squared error was achieved: 10.72 (UPDRS III), 2.16 (UPDRS IV), 4.76 (FOG-Q), 17.89 (NMSS), 2.13 (RBDSQ), 6.43 (ACE-R), 1.41 (MMSE), and 4.82 (BDI). These results show a promising potential of prosodic speech features in the field of objective assessment of PD severity.
Document typePeer reviewed
Document versionFinal PDF
SourceProceedings of the 22nd Conference STUDENT EEICT 2016. s. 533-537. ISBN 978-80-214-5350-0
- Student EEICT 2016