Extracting cues from speech for predicting severity of Parkinson's disease

Meysam Asgari, Izhak Shafran

Research output: Chapter in Book/Report/Conference proceedingConference contribution

19 Scopus citations

Abstract

Speech pathologists often describe voice quality in hypokinetic dysarthria or Parkinsonism as harsh or breathy, which has been largely attributed to incomplete closure of vocal folds. Exploiting its harmonic nature, we separate voiced portion of the speech to obtain an objective estimate of this quality. The utility of the proposed approach was evaluated on predicting 116 clinical ratings of Parkinson's disease on 82 subjects. Our results show that the information extracted from speech, elicited through 3 tasks, can predict the motor subscore (range 0 to 108) of the clinical measure, the Unified Parkinson's Disease Rating Scale, within a mean absolute error of 5.7 and a standard deviation of about 2.0. While still preliminary, our results are significant and demonstrate that the proposed computational approach has promising real-world applications such as in home-based assessment or in telemonitoring of Parkinson's disease.

Original languageEnglish (US)
Title of host publicationProceedings of the 2010 IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2010
Pages462-467
Number of pages6
DOIs
StatePublished - 2010
Event2010 IEEE 20th International Workshop on Machine Learning for Signal Processing, MLSP 2010 - Kittila, Finland
Duration: Aug 29 2010Sep 1 2010

Publication series

NameProceedings of the 2010 IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2010

Conference

Conference2010 IEEE 20th International Workshop on Machine Learning for Signal Processing, MLSP 2010
CountryFinland
CityKittila
Period8/29/109/1/10

ASJC Scopus subject areas

  • Human-Computer Interaction
  • Signal Processing

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