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

共 50 条

[31] Estimation of Severity in Parkinson's Disease Using Acoustic Features of Phonatory Tasks
Viswanathan, Rekha
Arjunan, Sridhar P.
IETE JOURNAL OF RESEARCH, 2023, 69 (09) : 6292 - 6303
[32] Heterogeneity in progression of prodromal features in Parkinson's disease
Schrag, A.
Zhelev, S. S.
Hotham, S.
Merritt, R. D.
Khan, K.
Graham, L.
PARKINSONISM & RELATED DISORDERS, 2019, 64 : 275 - 279
[33] IntegratedMRF: random forest-based framework for integrating prediction from different data types
Rahman, Raziur
Otridge, John
Pal, Ranadip
BIOINFORMATICS, 2017, 33 (09) : 1407 - 1410
[34] Learning acoustic features to detect Parkinson's disease
Wu, Kebin
Zhang, David
Lu, Guangming
Guo, Zhenhua
NEUROCOMPUTING, 2018, 318 : 102 - 108
[35] Random forest-based prediction of decay modes and half-lives of superheavy nuclei
Cai, Bo-Shuai
Yuan, Cen-Xi
NUCLEAR SCIENCE AND TECHNIQUES, 2023, 34 (12)
[36] Osteoarthritis Disease Prediction Based on Random Forest
Aprilliani, Ulfah
Rustam, Zuherman
2018 INTERNATIONAL CONFERENCE ON ADVANCED COMPUTER SCIENCE AND INFORMATION SYSTEMS (ICACSIS), 2018, : 237 - 240
[37] Comparison of Random Forest and Support Vector Machine for Prediction of Cognitive Impairment in Parkinson's Disease
Kibtia, Helmanita
Abdullah, Sarini
Bustamam, Alhadi
INTERNATIONAL CONFERENCE ON SCIENCE AND APPLIED SCIENCE (ICSAS2020), 2020, 2296
[38] A progression analysis of motor features in Parkinson's disease based on the mapper algorithm
Ma, Ling-Yan
Feng, Tao
He, Chengzhang
Li, Mujing
Ren, Kang
Tu, Junwu
FRONTIERS IN AGING NEUROSCIENCE, 2023, 15
[39] Prediction of Failure in Lubricated Surfaces Using Acoustic Time-Frequency Features and Random Forest Algorithm
Shevchik, Sergey A.
Saeidi, Fatemeh
Meylan, Bastian
Wasmer, Kilian
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS, 2017, 13 (04) : 1541 - 1553
[40] Random forest-based prediction of decay modes and half-lives of superheavy nuclei
Bo-Shuai Cai
Cen-Xi Yuan
Nuclear Science and Techniques, 2023, 34

← 1 2 3 4 5 →