Name
Title | ||
|---|---|---|
Random Forest-Based Prediction Of Parkinson'S Disease Progression Using Acoustic, Asr And Intelligibility Features |
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. |
Year | Venue | Keywords |
|---|---|---|
2015 | 16TH ANNUAL CONFERENCE OF THE INTERNATIONAL SPEECH COMMUNICATION ASSOCIATION (INTERSPEECH 2015), VOLS 1-5 | random forest, regression, Parkinson's disease, ASR features, intelligibility |
Field | DocType | Citations |
Parkinson's disease,Pattern recognition,Regression,Computer science,Speech recognition,Artificial intelligence,Random forest,Ensemble learning,S Voice,Intelligibility (communication) | Conference | 1 |
PageRank | References | Authors |
0.37 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Alexander Zlotnik | 1 | 2 | 1.10 |
| Juan Manuel Montero | 2 | 218 | 31.51 |
| Rubén San-Segundo-Hernández | 3 | 173 | 29.60 |
| j maciasguarasa | 4 | 92 | 19.30 |