Identification of Pronunciation Defects in Spoken Arabic Language

  • Naim TerbehEmail author
  • Mounir Zrigui
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 781)


The detection of vocal pathologies is one of the novelties addressing automatic speech processing. There are several intervening approaches that are based on features contained in an acoustic signal and on natural language processing techniques. However, up to our knowledge, these studies are not extended to detect phonemes that pose degraded speeches. In this paper, we propose a new method to detect mispronounced sounds. We are based on a phonetic-probabilistic modeling. The invented study accounts four fundamental tasks. The first task summarizes the calculation of the probabilistic-phonetic model referring to Arabic speech. The second one is dedicated to calculate the probabilistic-phonetic model appropriate to a speaker whose elocution is classified as pathological. Thirdly, we compare between the two previous models to distinguish two main classes: the input speech can be healthy or pathological. The fourth stage consists in introducing an original algorithm based on a phonetic modeling to generate problematic sounds and to evaluate the elocution of each speaker having voice pathologies by attributing them a language level. This task will be only applied if the input speech is pathological. The obtained results are satisfactory. We have attained a problematic-sound identification rate of 96%.


Problematic sounds Arabic healthy/pathological speech Probabilistic-phonetic modeling Pronunciation defects Elocution evaluation 


  1. 1.
    Terbeh, N., Maraoui, M., Zrigui, M.: Probabilistic approach for detection of vocal pathologies in the arabic speech. In: CICLing 2015, Cairo-Egypt, 14–20 April 2015 (2015)Google Scholar
  2. 2.
    Alghamdi, M., Almuhtasib, H., Elshafei, M.: Arabic phonological rules. King Saud Univ. J. Comput. Sci. Inf. 16, 1–25 (2004)Google Scholar
  3. 3.
    Terbeh, N., Labidi, M., Zrigui, M.: Automatic speech correction: a step to speech recognition for people with disabilities. In: ICTA 2013, Hammamet-Tunisia, 23–26 October 2013 (2013)Google Scholar
  4. 4.
    Terbeh, N., Zrigui, M.: Vers la Correction Automatique de la Parole Arabe. Citala 2014, Oujda-Morocco, 26–27 November 2014 (2014)Google Scholar
  5. 5.
    Patane, G., Russo, M.: The enhanced LBG algorithm. Neural Netw. 14(9), 1219–1237 (2001)Google Scholar
  6. 6.
    Bréhilin, L., Gascuel, O.: Modèles de Markov caches et apprentissage de séquencesGoogle Scholar
  7. 7.
    Majidnezhad, V., Kheidorov, I.: An ANN-based method for detecting vocal fold pathology. Int. J. Comput. Appl. 62(7), 1–4 (2013)Google Scholar
  8. 8.
    Karsenty, A.: Le collecticiel: De l’interaction homme-machine à la communication homme-machine-homme (1994)Google Scholar
  9. 9.
    Majidnezhad, V., Kheidorov, I.: A HMM-based method for vocal fold pathology diagnosis. IJCSI Int. J. Comput. Sci. Issues 9(6), 2 (2012)Google Scholar
  10. 10.
    Daniel, S.: De l’interaction homme-machine individuelle aux systèmes multi-utilisateurs (L’exemple de la communication homme-homme médiatisée). Doctoral thesis (1995)Google Scholar
  11. 11.
    Paquet, P.: L’utilisation des réseaux de neurones artificiels en finance. Document de recherche n° 1997-1 (1997)Google Scholar
  12. 12.
    Terbeh, N., Zrigui, M.: Vocal pathologies detection and mispronounced phonemes identification: case of Arabic continuous speech. In: LREC 2016, Portorož-Slovenia, 23–28 May 2016 (2016)Google Scholar
  13. 13.
  14. 14.
    Belgacem, M.: Reconnaissance automatique de la parole et ALAO: Vers un système d’apprentissage de l’arabe oral. Ph.D. thesis (2011)Google Scholar
  15. 15.
    Biadsy, F., Hirschberg, J., Habash, N.: Spoken Arabic dialect identification using phonotactic modeling. In: Proceedings of the EACL 2009 Workshop on Computational Approaches to Semitic Languages, pp. 53–61. Association for Computational Linguistics (2009)Google Scholar
  16. 16.
    Elshafei, M., Almuhtasib, H., Alghamdi, M.: Statistical methods for automatic diacritization of Arabic text. In: Proceedings of 18th National computer Conference, Riyadh, Saudi Arabia (2006)Google Scholar
  17. 17.
    Muhammad, G., Alsulaiman, M., Ali, Z., Malki, K., Mesallam, T., Farahat, M.: Voice pathology detection and classification using auto-correlation and entropy features in different frequency regions. IEEE Access PP(99), 1 (2017)Google Scholar
  18. 18.
    Lin, J., Xie, Y., Zhang, J.: Automatic pronunciation evaluation of non-native Mandarin tone by using multi-level confidence measures. In: Interspeech 2016Google Scholar
  19. 19.
    Patil, V.V., Rao, P.: Detection of phonemic aspiration for spoken Hindi pronunciation evaluation. J. Phonetics 54, 202–221 (2016)CrossRefGoogle Scholar

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© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  1. 1.LaTICE LaboratoryFaculty of Sciences of MonastirMonastirTunisia

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