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Speaker Model to Monitor the Neurological State and the Dysarthria Level of Patients with Parkinson’s Disease

  • J. C. Vásquez-CorreaEmail author
  • R. Castrillón
  • T. Arias-Vergara
  • J. R. Orozco-Arroyave
  • E. Nöth
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10415)

Abstract

The progression of the disease in Parkinson’s patients is commonly evaluated with the unified Parkinson’s disease rating scale (UPDRS), which contains several items to assess motor and non–motor impairments. The patients develop speech impairments that can be assessed with a scale to evaluate dysarthria. Continuous monitoring of the patients is suitable to update the medication or the therapy. In this study, a robust speaker model based on the GMM–UBM approach is proposed for the continuous monitoring of the state of Parkinson’s patients. The model is trained with phonation, articulation, and prosody features with the aim of evaluating deficits on each speech dimension. The performance of the model is evaluated in two scenarios: the monitoring of the UPDRS score and the prediction of the dysarthria level of the speakers. The results indicate that the speaker models are suitable to track the disease progression, specially in terms of the evaluation of the dysarthia level of the speakers.

Keywords

Parkinson’s disease UPDRS Dysarthria Phonation Articulation Prosody Speaker model 

Notes

Acknowledgments

The work reported here was started at JSALT 2016, and was supported by JHU via grants from DARPA (LORELEI), Microsoft, Amazon, Google and Facebook. Thanks also to CODI from University of Antioquia by the grant Numbers 2015–7683 and PRV16-2-01.

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • J. C. Vásquez-Correa
    • 1
    • 3
    Email author
  • R. Castrillón
    • 2
  • T. Arias-Vergara
    • 1
  • J. R. Orozco-Arroyave
    • 1
    • 3
  • E. Nöth
    • 3
  1. 1.Faculty of EngineeringUniversidad de Antioquia UdeAMedellínColombia
  2. 2.Universidad Católica de OrienteRionegroColombia
  3. 3.Pattern Recognition LabFriedrich-Alexander-Universität Erlangen-NürnbergErlangenGermany

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