RT Conference Proceedings
T1 Random forest-based prediction of Parkinson's disease progression using acoustic, ASR and intelligibility features
A1 Zlotnik, Alexander
A1 Montero Martínez, Juan Manuel
A1 San Segundo Hernández, Rubén
A1 Gallardo Antolín, Ascensión
AB 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.
PB International Speech Communication Association (ISCA)
YR 2015
FD 2015
LK https://hdl.handle.net/10016/31990
UL https://hdl.handle.net/10016/31990
LA eng
NO Proceeding of: 16th Annual Conference of the International Speech Communication Association (INTERSPEECH 2015), Dresden, Germany, September 6-10, 2015
NO The work leading to these results has been partly supported by Spanish Government grants TEC2014-53390-P and DPI2014-53525-C3-2-R, and from the European Union under grant agreement number 287678 (SIMPLE4ALL). Authors also thank all the other members of the Speech Technology Group at UPM and Grupo de Procesado Multimedia at UC3M for the continuous and fruitful discussion on these topics.
DS e-Archivo
RD 1 jul. 2024