Publication: Random forest-based prediction of Parkinson's disease progression using acoustic, ASR and intelligibility features
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2015
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International Speech Communication Association (ISCA)
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Abstract
The Interspeech ComParE 2015 PC Sub-Challenge consists of automatically determining the degree of Parkinson's condition using exclusively the patient's voice. In this paper, we face this problem as a regression task and in order to succeed, we propose the use of an ensemble learning method, Random Forest (RF), in combination with features of different nature: acoustic characteristics, features derived from the output of an Automatic Speech Recognition system (ASR) and non-intrusive intelligibility measures. The system outperforms the baseline results achieving a relative improvement higher than 19% in the development set.
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Proceeding of: 16th Annual Conference of the International Speech Communication Association (INTERSPEECH 2015), Dresden, Germany, September 6-10, 2015
Keywords
Random forest, Regression, Parkinson's disease, ASR features, Intelligibility
Bibliographic citation
INTERSPEECH 2015: 16th Annual Conference of the International Speech Communication Association, Dresden, Germany, September 6-10, 2015. ISCA, 2015, Pp. 503-507