Automatic voice disorder classification using vowel formants

Ghulam Muhammad, Mansour Alsulaiman, Awais Mahmood, Zulfiqar Ali

Research output: Contribution to conferencePaperpeer-review

31 Citations (Scopus)


In this paper, we propose an automatic voice disorder classification system using first two formants of vowels. Five types of voice disorder, namely, cyst, GERD, paralysis, polyp and sulcus, are used in the experiments. Spoken Arabic digits from the voice disordered people are recorded for input. First formant and second formant are extracted from the vowels [Fatha] and [Kasra], which are present in Arabic digits. These four features are then used to classify the voice disorder using two types of classification methods: vector quantization (VQ) and neural networks. In the experiments, neural network performs better than VQ. For female and male speakers, the classification rates are 67.86% and 52.5%, respectively, using neural networks. The best classification rate, which is 78.72%, is obtained for female sulcus disorder.

Original languageEnglish
Number of pages6
Publication statusPublished (in print/issue) - 7 Nov 2011
Event2011 12th IEEE International Conference on Multimedia and Expo, ICME 2011 - Barcelona, Spain
Duration: 11 Jul 201115 Jul 2011


Conference2011 12th IEEE International Conference on Multimedia and Expo, ICME 2011


  • Arabic digit
  • Arabic vowel
  • automatic classification of voice disorder
  • neural network


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