Potential of Prosodic Features to Estimate Degree of Parkinson's Disease Severity
Alternative metrics PlumXhttp://hdl.handle.net/11012/83990
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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
SourceProceedings of the 22st Conference STUDENT EEICT 2016. s. 533-537. ISBN 978-80-214-5350-0
- Student EEICT 2016