Random Forest-Based Prediction of Parkinson's Disease Progression Using Acoustic, ASR and Intelligibility Features

被引：0
|
作者：
Zlotnik, Alexander [1 ]
Montero, Juan M. [1 ]
San-Segundo, Ruben [1 ]
Gallardo-Antolin, Ascension [2 ]
机构：
[1] Univ Politecn Madrid, Speech Technol Grp, ETSIT, E-28040 Madrid, Spain
[2] Univ Carlos III Madrid, Dept Signal Theory & Commun, E-28903 Getafe, Spain
来源：
16TH ANNUAL CONFERENCE OF THE INTERNATIONAL SPEECH COMMUNICATION ASSOCIATION (INTERSPEECH 2015), VOLS 1-5 | 2015年
关键词：
random forest; regression; Parkinson's disease; ASR features; intelligibility;
D O I：
暂无
中图分类号：
O42 [声学];
学科分类号：
070206 ; 082403 ;
摘要：
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.
引用
收藏
页码：503 / 507
页数：5
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