Vocal fold disorder detection based on continuous speech by using MFCC and GMM

Zulfiqar Ali, Mansour Alsulaiman, Ghulam Muhammad, Irraivan Elamvazuthi, Tamer A. Mesallam

Research output: Contribution to conferencePaperpeer-review

29 Citations (Scopus)


Vocal fold voice disorder detection with a sustained vowel is well investigated by research community during recent years. The detection of voice disorder with a sustained vowel is a comparatively easier task than detection with continuous speech. The speech signal remains stationary in case of sustained vowel but it varies over time in continuous time. This is the reason; voice detection by using continuous speech is challenging and demands more investigation. Moreover, detection with continuous speech is more realistic because people use it in their daily conversation but sustained vowel is not used in everyday talks. An accurate voice assessment can provide unique and complementary information for the diagnosis, and can be used in the treatment plan. In this paper, vocal fold disorders, cyst, polyp, nodules, paralysis, and sulcus, are detected using continuous speech. Mel-frequency cepstral coefficients (MFCC) are used with Gaussian mixture model (GMM) to build an automatic detection system capable of differentiating normal and pathological voices. The detection rate of the developed detection system with continuous speech is 91.66%.

Original languageEnglish
Number of pages6
Publication statusPublished (in print/issue) - 1 Dec 2013
Event2013 7th IEEE GCC Conference and Exhibition, GCC 2013 - Doha, Qatar
Duration: 17 Nov 201320 Nov 2013


Conference2013 7th IEEE GCC Conference and Exhibition, GCC 2013


  • continuous speech
  • GMM
  • MFCC
  • pathology detection
  • Voice disorder

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