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

页码：503 / 507

页数：5

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