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Effect of Acoustic Conditions on Algorithms to Detect Parkinson’s Disease from Speech

Abstract: 

Automatic detection of Parkinson's disease (PD) from speech is a basic step towards computer-aided tools supporting the diagnosis and monitoring of the disease. Although several methods have been proposed, their applicability to real-world situations is still unclear. In particular, the effect of acoustic conditions is not well understood. In this paper, the effects on the accuracy of five different methods to detect PD from speech are evaluated. Among the considered conditions, background noise produces the worst effect, while dynamic compression or some speech codecs can even have a marginal positive impact. We also consider, for the first time in this context, the problem of mismatches, i.e.,~when train/test acoustic conditions are different, and observe a high negative impact on all considered methods.
Overall, this study is a step forward in performing a continuous monitoring of the neurological state of the patients in non-controlled acoustic conditions.

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Paper Details

Authors:
Juan Camilo Vásquez-Correa, Joan Serrà, Juan Rafael Orozco-Arroyave, Jesus Francisco Vargas-Bonilla, Elmar Nöh
Submitted On:
5 March 2017 - 10:41am
Short Link:
Type:
Presentation Slides
Event:
Presenter's Name:
Juan Camilo Vasquez Correa
Paper Code:
3049
Document Year:
2017
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Document Files

EffectAcousticConditionsParkinsonSpeech.pdf

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[1] Juan Camilo Vásquez-Correa, Joan Serrà, Juan Rafael Orozco-Arroyave, Jesus Francisco Vargas-Bonilla, Elmar Nöh, "Effect of Acoustic Conditions on Algorithms to Detect Parkinson’s Disease from Speech", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/1634. Accessed: Jul. 19, 2019.
@article{1634-17,
url = {http://sigport.org/1634},
author = {Juan Camilo Vásquez-Correa; Joan Serrà; Juan Rafael Orozco-Arroyave; Jesus Francisco Vargas-Bonilla; Elmar Nöh },
publisher = {IEEE SigPort},
title = {Effect of Acoustic Conditions on Algorithms to Detect Parkinson’s Disease from Speech},
year = {2017} }
TY - EJOUR
T1 - Effect of Acoustic Conditions on Algorithms to Detect Parkinson’s Disease from Speech
AU - Juan Camilo Vásquez-Correa; Joan Serrà; Juan Rafael Orozco-Arroyave; Jesus Francisco Vargas-Bonilla; Elmar Nöh
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/1634
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Juan Camilo Vásquez-Correa, Joan Serrà, Juan Rafael Orozco-Arroyave, Jesus Francisco Vargas-Bonilla, Elmar Nöh. (2017). Effect of Acoustic Conditions on Algorithms to Detect Parkinson’s Disease from Speech. IEEE SigPort. http://sigport.org/1634
Juan Camilo Vásquez-Correa, Joan Serrà, Juan Rafael Orozco-Arroyave, Jesus Francisco Vargas-Bonilla, Elmar Nöh, 2017. Effect of Acoustic Conditions on Algorithms to Detect Parkinson’s Disease from Speech. Available at: http://sigport.org/1634.
Juan Camilo Vásquez-Correa, Joan Serrà, Juan Rafael Orozco-Arroyave, Jesus Francisco Vargas-Bonilla, Elmar Nöh. (2017). "Effect of Acoustic Conditions on Algorithms to Detect Parkinson’s Disease from Speech." Web.
1. Juan Camilo Vásquez-Correa, Joan Serrà, Juan Rafael Orozco-Arroyave, Jesus Francisco Vargas-Bonilla, Elmar Nöh. Effect of Acoustic Conditions on Algorithms to Detect Parkinson’s Disease from Speech [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/1634