Automatic Detection of Parkinson’s Disease from Compressed Speech Recordings

  • Juan Rafael Orozco-Arroyave
  • Nicanor García
  • Jesús Francisco Vargas-Bonilla
  • Elmar Nöth
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9302)

Abstract

The impact of speech compression in the automatic classification of speakers with Parkinson’s disease (PD) and healthy controls (HC) is tested. The set of codecs considered to compress the speech recordings includes G.722, G.226, GSM-EFR, AMR-WB, SILK, and Opus. A total of 100 speakers (50 with PD and 50 HC) are asked to read a text with 36 words. The recordings are compressed from bit-rates of 705.6 kbps down to 6.6 kbps. The method addressed to discriminate between speakers with PD and HC consists on the systematic segmentat ion of voiced and unvoiced speech frames. Each kind of frame is characterized independently. For voiced segments noise, perturbation, and cepstral features are considered. The unvoiced segments are characterized with Bark band energies and cepstral features. According to the results the codecs evaluated in this paper do not affect significantly the accuracy of the system, indicating that the addressed methodology could be used for the telemonitoring of PD patients through Internet or through the mobile communications network.

Keywords

Parkinson’s disease Speech compression Speech codec Voiced/unvoiced frames Internet Telemonitoring Mobile communications network 

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Juan Rafael Orozco-Arroyave
    • 1
    • 2
  • Nicanor García
    • 2
  • Jesús Francisco Vargas-Bonilla
    • 2
  • Elmar Nöth
    • 1
  1. 1.Pattern Recognition LabFriedrich-Alexander-UniversitätErlangenGermany
  2. 2.Faculty of EngineeringUniversidad de AntioquiaMedellínColombia

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