Abstract
Literature seems rich with approaches which are based on the features contained in the speech signal and natural language processing techniques to detect vocal pathologies in human speeches. From the literature, we can mention also that several factors (vocal pathology, non-native speaker, psychological state, age …) can pose pronunciation disorders [10]. But to our knowledge, no work has treated pathological speech to identify factor posing pronunciation disorders. The current work consists in introducing an original approach based on the forced alignment score [8] to identify the factor posing mispronunciations contained in the Arabic speech. We distinguish two main factors: the pronunciation disorders can be from native speakers with vocal pathology or from non-native speakers who do not master Arabic-phoneme pronunciation. The results are encouraging; we attain an identification rate of 95 %. Biologists and computer scientists can benefit from our proposed approach to design high performance systems of vocal pathology diagnostic.
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References
Terbeh, N., Maraoui, M., Zrigui, M.: Probabilistic approach for detection of vocal pathologies in the Arabic speech. In: Gelbukh, A. (ed.). LNCS, vol. 9042, pp. 606–616. Springer, Heidelberg (2015)
Alghamdi, M., Almuhtasib, H., Elshafei, M.: Arabic phonological rules. King Saud Univ. J. Comput. Sci. Inf. 16, 1–25 (2004)
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)
Terbeh, N., Zrigui, M.: Vers la Correction Automatique de la Parole Arabe. In: Citala 2014, Oujda-Morocco, 26–27 November 2014 (2014)
Patane, G., Russo, M.: The enhanced LBG algorithm. Neural Netw. 14(9), 1219–1237 (2001)
Bréhilin, L., Gascuel, O.: Modèles de Markov caches et apprentissage de sequences
Majidnezhad, V., Kheidorov, I.: An ANN-based method for detecting vocal fold pathology. Int. J. Comput. Appl. 62(7), 1–4 (2013)
Jurafsky, D., Ward, W., Zhang, B., Herold, K., Yu, X., Zhang, S.: What kind of pronunciation variation is hard for triphones to model? In: ICASSP 2001, Salt Lake City, UT, 7–11 May 2001
Majidnezhad, V., Kheidorov, I.: A HMM-based method for vocal fold pathology diagnosis. IJCSI Int. J. Comput. Sci. Issues 9(6), 135–138 (2012). No. 2
Kim, J., Kumar, N., Tsiartas, A., Li, M., Narayanan, S.: Intelligibility classification of pathological speech using fusion of multiple subsystems. In: Proceedings of Interspeech, Portland, Oregon, USA, pp. 534–537 (2012)
Paquet, P.: L’utilisation des réseaux de neurones artificiels en finance. Document de recherche n° 1997-1 (1997)
Archaux, C., Laanaya, H., Martin, A., Khenchaf, A.: An SVM based churn detector in prepaid mobile telephony (2004)
Kukharchik, P., Martynov, D., Kheidorov, I., Kotov, O.: Vocal fold pathology detection using modified wavelet-like features and support vector machines. In: 15th European Signal Processing Conference (EUSIPCO 2007), Poznan, Poland, 3–7 September 2007
Damerval, C.: Ondelettes pour la détection de caractéristiques en traitement d’images. Doctoral thesis, Mai 2008
Plante, F., Christian, B.-V.: Détection acoustique des pathologies phonatoires chez l’enfant. Doctoral thesis (1993)
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)
http://www.un.org/french/disabilities/default.asp?navid=35&pid=833, [consulted 6 April 2016]
http://kenanaonline.com/users/dkkhaledelnagar/photos/1238136361, [consulted 24 April 2016]
Blanc-Brude, T.: Intégration de commandes vocales dans un environnement d’apprentissage par l’action: enjeux ergonomiques. Doctoral dissertation, Grenoble 1 (2004)
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)
Acknowledgments
We would like to benefit from this opportunity to express my deepest regards to all members of the evaluation research committee in the ICCCI scientific conference. We would like also to extend our advance thanks to Mr. Mounir ZRIGUI for his valuable advices and encouragement.
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Terbeh, N., Zrigui, M. (2016). A Novel Approach to Identify Factor Posing Pronunciation Disorders. In: Nguyen, NT., Iliadis, L., Manolopoulos, Y., Trawiński, B. (eds) Computational Collective Intelligence. ICCCI 2016. Lecture Notes in Computer Science(), vol 9875. Springer, Cham. https://doi.org/10.1007/978-3-319-45243-2_14
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DOI: https://doi.org/10.1007/978-3-319-45243-2_14
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