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Visualization of Voice Disorders Using the Sammon Transform

  • Tino Haderlein
  • Dominik Zorn
  • Stefan Steidl
  • Elmar Nöth
  • Makoto Shozakai
  • Maria Schuster
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4188)

Abstract

The Sammon Transform performs data projections in a topology-preserving manner on the basis of an arbitrary distance measure. We use the weights of the observation probabilities of semi-continuous HMMs that were adapted to the current speaker as input. Experiments on laryngectomized speakers with tracheoesophageal substitute voice, hoarse, and normal speakers show encouraging results. Different speaker groups are separated in 2-D space, and the projection of a new speaker into the Sammon map allows prediction of his or her kind of voice pathology. The method can thus be used as an objective, automated support for the evaluation of voice disorders, and it visualizes them in a way that is convenient for speech therapists.

Keywords

Speech Recognition Speech Recognition System Speech Effort Voice Disorder Speech Recognizer 
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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References

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Tino Haderlein
    • 1
  • Dominik Zorn
    • 2
  • Stefan Steidl
    • 2
  • Elmar Nöth
    • 2
  • Makoto Shozakai
    • 3
  • Maria Schuster
    • 1
  1. 1.Department of Phoniatrics and PedaudiologyUniversity of Erlangen-NurembergErlangenGermany
  2. 2.Chair for Pattern Recognition (Informatik 5)University of Erlangen-NurembergErlangenGermany
  3. 3.Speech Recognition DepartmentAsahi Kasei CorporationKanagawaJapan

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