Zlotnik, AlexanderMontero Martínez, Juan ManuelSan Segundo Hernández, RubénGallardo Antolín, Ascensión2021-02-222021-02-222015INTERSPEECH 2015: 16th Annual Conference of the International Speech Communication Association, Dresden, Germany, September 6-10, 2015. ISCA, 2015, Pp. 503-507https://hdl.handle.net/10016/31990Proceeding of: 16th Annual Conference of the International Speech Communication Association (INTERSPEECH 2015), Dresden, Germany, September 6-10, 2015The 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.5eng© 2015 ISCA.Random forestRegressionParkinson's diseaseASR featuresIntelligibilityconference paperBiología y BiomedicinaElectrónicaTelecomunicacionesopen access503507INTERSPEECH 2015: 16th Annual Conference of the International Speech Communication Association, Dresden, Germany, September 6-10, 2015CC/0000024912