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Dysarthric Speech Classification Using Hierarchical Multilayer Perceptrons and Posterior Rhythmic Features

  • Sid-Ahmed Selouani
  • Habiba Dahmani
  • Riadh Amami
  • Habib Hamam
Part of the Advances in Intelligent and Soft Computing book series (AINSC, volume 87)

Abstract

In this paper class posterior distributions are combined with a hierarchal structure of multilayer Perceptrons to perform an automatic assessment of dysarthric speech. In addition to the standard Mel-frequency coefficients, this hybrid classifier uses rhythm-based features as input parameters since the preliminary evidence from perceptual experiments show that rhythm troubles may be the common characteristic of various types of dysarthria. The Nemours database of American dysarthric speakers is used throughout experiments. Results show the relevance of rhythm metrics and the effectiveness of the proposed hybrid classifier to discriminate the levels of dysarthria severity.

Keywords

Cerebral Palsy Speech Signal Gaussian Mixture Model Automatic Assessment Phoneme Recognition 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Sid-Ahmed Selouani
    • 1
  • Habiba Dahmani
    • 1
  • Riadh Amami
    • 3
  • Habib Hamam
    • 2
  1. 1.Université de MonctonNew BrunswickCanada
  2. 2.INRS-Université du QuébecMontrealCanada
  3. 3.École ESPRITTunisTunisia

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