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.
Bibliographic reference. Zlotnik, Alexander / Montero, Juan M. / San-Segundo, Rubén / Gallardo-Antolín, Ascensión (2015): "Random forest-based prediction of parkinson's disease progression using acoustic, ASR and intelligibility features", In INTERSPEECH-2015, 503-507.