Classification of Psychogenic and Laryngeal Voice Diseases Based on Teager Energy Operator

Year 2019, , 49 - 55, 30.09.2019

İmen Hammami

https://doi.org/10.18100/ijamec.458230

Abstract

• Among several ways of communication, the voice remains the fastest
  natural tool for human-to-human and human-to-machine communication. That is why
  the research in automatic voice pathology detection and classification area has
  gained much interest in the recent years. Indeed, these automatic systems may
  be considered as assistive tools for the physicians during the assessment
  stage. This latter may help them to make decision, whether the voice signal
  belongs to a healthy or unhealthy subject and identifies the nature of
  pathology. In this context, this paper provides a voice pathology detection and
  classification system based on wavelet analysis and Teager Energy Operator
  (TEO). First, we used the input voice signal that we taken from Saarbrücken
  Voice Database (SVD) [1], to extract a set of features. These feature vectors
  are fed into a Gaussian Mixture Model (GMM) [2] for the sake of classification.
  The obtained results are 96.66% for the detection task and 92.5 % using TEO.
  These results show that our proposal outperforms some state-of-art methods used
  in voice pathology identification.

Keywords

Pathological Voices identification, SVD corpus, Wavelet Analysis, Teager Energy Operator

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