TY - GEN
T1 - Extracting cues from speech for predicting severity of Parkinson's disease
AU - Asgari, Meysam
AU - Shafran, Izhak
N1 - Copyright:
Copyright 2010 Elsevier B.V., All rights reserved.
PY - 2010
Y1 - 2010
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=78449290172&partnerID=8YFLogxK
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U2 - 10.1109/MLSP.2010.5589118
DO - 10.1109/MLSP.2010.5589118
M3 - Conference contribution
AN - SCOPUS:78449290172
SN - 9781424478774
T3 - Proceedings of the 2010 IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2010
SP - 462
EP - 467
BT - Proceedings of the 2010 IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2010
T2 - 2010 IEEE 20th International Workshop on Machine Learning for Signal Processing, MLSP 2010
Y2 - 29 August 2010 through 1 September 2010
ER -