Automatic Detection of Parkinson’s Disease in Reverberant Environments

  • Juan Rafael Orozco-Arroyave
  • Tino Haderlein
  • Elmar Nöth
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9302)


Automatic classification of speakers with Parkinson’s disease (PD) and healthy controls (HC) is performed considering a method for the characterization of the speech signals which is based on the estimation of the energy content of the unvoiced frames. The method is tested with recordings of three languages: Spanish, German, and Czech. Additionally, the signals are affected by two different reverberant scenarios in order to validate the robustness of the proposed method. The obtained results range from \(85\%\) to \(99\%\) of accuracy depending on the speech task, the spoken language, and the recording scenario. The method shows to be accurate and robust even when the signals are reverberated. This work is a step forward to the development of methods to assess the speech of PD patients without requiring special acoustic conditions.


Parkinson’s disease Reverberant evironments Unvoiced frames Multi-language 


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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Juan Rafael Orozco-Arroyave
    • 1
    • 2
  • Tino Haderlein
    • 2
  • Elmar Nöth
    • 2
  1. 1.Faculty of EngineeringUniversidad de Antioquia UdeAMedellínColombia
  2. 2.Pattern Recognition LabFriedrich-Alexander-Universität Erlangen-Nürnberg (FAU)ErlangenGermany

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