Automatic voice pathology detection and classification using vocal tract area irregularity

Ghulam Muhammad, Ghadir Altuwaijri, Mansour Alsulaiman, Zulfiqar Ali, Tamer A. Mesallam, Mohamed Farahat, Khalid H. Malki, Ahmed Al-Nasheri

Research output: Contribution to journalArticlepeer-review

45 Citations (Scopus)
199 Downloads (Pure)


In this paper, an automatic voice pathology detection (VPD) system based on voice production theory is developed. More specifically, features are extracted from vocal tract area, which is connected to the glottis. Voice pathology is related to a vocal fold problem, and hence the vocal tract area which is connected to vocal folds or glottis should exhibit irregular patterns over frames in case of a sustained vowel for a pathological voice. This irregular pattern is quantified in the form of different moments across the frames to distinguish between normal and pathological voices. The proposed VPD system is evaluated on the Massachusetts Eye and Ear Infirmary (MEEI) database and Saarbrucken Voice Database (SVD) with sustained vowel samples. Vocal tract irregularity features and support vector machine classifier are used in the proposed system. The proposed system achieves 99.22% ± 0.01 accuracy on the MEEI database and 94.7% ± 0.21 accuracy on the SVD. The results indicate that vocal tract irregularity measures can be used effectively in automatic voice pathology detection.

Original languageEnglish
Pages (from-to)309-317
Number of pages9
JournalBiocybernetics and Biomedical Engineering
Issue number2
Early online date28 Jan 2016
Publication statusPublished (in print/issue) - 31 Jan 2016


  • Support vector machine
  • Vocal tract area
  • Voice disorders
  • Voice pathology detection


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