Language Independent Assessment of Motor Impairments of Patients with Parkinson’s Disease Using i-Vectors

  • N. GarciaEmail author
  • J. C. Vásquez-Correa
  • J. R. Orozco-Arroyave
  • N. Dehak
  • E. Nöth
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10415)


Speech disorders are among the most common symptoms in patients with Parkinson’s disease. In recent years, several studies have aimed to analyze speech signals to detect and to monitor the progression of the disease. Most studies have analyzed speakers of a single language, even in that scenario the problem remains open. In this study, a cross-language experiment is performed to evaluate the motor impairments of the patients in three different languages: Czech, German and Spanish. The i-vector approach is used for the evaluation due to its capability to model speaker traits. The cosine distance between the i-vector of a test speaker and a reference i-vector that represents either healthy controls or patients is computed. This distance is used to perform two analyses: classification between patients and healthy speakers, and the prediction of the neurological state of the patients according to the MDS-UPDRS score. Classification accuracies of up to \(72\%\) and Spearman’s correlations of up to 0.41 are obtained between the cosine distance and the MDS-UPDRS score. This study is a step towards a language independent assessment of patients with neuro-degenerative disorders.


Parkinson’s disease i-vectors UPDRS score Language independent assessment 



Thanks to CODI from University of Antioquia by the grant Numbers 2015-7683 and PRV16-2-01 and to COLCIENCIAS by the grant Number 111556933858.


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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • N. Garcia
    • 1
    Email author
  • J. C. Vásquez-Correa
    • 1
    • 2
  • J. R. Orozco-Arroyave
    • 1
    • 2
  • N. Dehak
    • 3
  • E. Nöth
    • 2
  1. 1.Faculty of EngineeringUniversidad de Antioquia UdeAMedellínColombia
  2. 2.Pattern Recognition LabFriedrich-Alexander-Universität Erlangen-NürnbergErlangenGermany
  3. 3.Center for Language and Speech ProcessingJhons Hopkins UniversityBaltimoreUSA

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