Automatic detection of Parkinson's disease in running speech spoken in three different languages

The aim of this study is the analysis of continuous speech signals of people with Parkinson's disease (PD) considering recordings in different languages (Spanish, German, and Czech). A method for the characterization of the speech signals, based on the automatic segmentation of utterances into...

Full description

Saved in:
Bibliographic Details
Published in:The Journal of the Acoustical Society of America 2016-01, Vol.139 (1), p.481-500
Main Authors: Orozco-Arroyave, J. R., Hönig, F., Arias-Londoño, J. D., Vargas-Bonilla, J. F., Daqrouq, K., Skodda, S., Rusz, J., Nöth, E.
Format: Article
Language:English
Subjects:
Adult
Aged
Aged, 80 and over
Area Under Curve
Czech Republic
Female
Germany
Humans
Language
Male
Middle Aged
Parkinson Disease - diagnosis
Parkinson Disease - physiopathology
Phonetics
Reading
Recognition (Psychology)
Spain
Speech - physiology
Speech Acoustics
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:The aim of this study is the analysis of continuous speech signals of people with Parkinson's disease (PD) considering recordings in different languages (Spanish, German, and Czech). A method for the characterization of the speech signals, based on the automatic segmentation of utterances into voiced and unvoiced frames, is addressed here. The energy content of the unvoiced sounds is modeled using 12 Mel-frequency cepstral coefficients and 25 bands scaled according to the Bark scale. Four speech tasks comprising isolated words, rapid repetition of the syllables /pa/-/ta/-/ka/, sentences, and read texts are evaluated. The method proves to be more accurate than classical approaches in the automatic classification of speech of people with PD and healthy controls. The accuracies range from 85% to 99% depending on the language and the speech task. Cross-language experiments are also performed confirming the robustness and generalization capability of the method, with accuracies ranging from 60% to 99%. This work comprises a step forward for the development of computer aided tools for the automatic assessment of dysarthric speech signals in multiple languages.
ISSN:0001-4966
1520-8524
DOI:10.1121/1.4939739