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Strategies for Implementing an Optimal ASR System for Quranic Recitation Recognition

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2017
Mohamed O. M. Khelifa, Mostafa Belkasmi, Yahya O. Mohamed Elhadj, Yousfi Abdellah

Mohamed O M Khelifa, Mostafa Belkasmi, Yahya Mohamed O Elhadj and Yousfi Abdellah. Strategies for Implementing an Optimal ASR System for Quranic Recitation Recognition. International Journal of Computer Applications 172(9):35-41, August 2017. BibTeX

	author = {Mohamed O. M. Khelifa and Mostafa Belkasmi and Yahya O. Mohamed Elhadj and Yousfi Abdellah},
	title = {Strategies for Implementing an Optimal ASR System for Quranic Recitation Recognition},
	journal = {International Journal of Computer Applications},
	issue_date = {August 2017},
	volume = {172},
	number = {9},
	month = {Aug},
	year = {2017},
	issn = {0975-8887},
	pages = {35-41},
	numpages = {7},
	url = {},
	doi = {10.5120/ijca2017915209},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}


With the help of automatic speech recognition (ASR) techniques, computers become capable of recognizing speech. The Quran is the speech of Allah (The God); it is the Holy book for all Muslims in the world; it is written and recited in Classical Arabic language, the language in which it was revealed by Allah to the Prophet Muhammad. Knowing how to pronounce correctly the Quranic sounds and correct mistakes occurred in reading is one of the most important topics in Quranic ASR applications, which assist self-learning, memorizing and checking the Holy Quran recitations. This paper presents a practical framework for development and implementation of an optimal ASR system for Quranic sounds recognition. The system uses the statistical approach of Hidden Markov Models (HMMs) for modeling the Quranic sounds and the Cambridge HTK tools as a development environment. Since sounds duration is regarded as a distinguishing factor in Quranic recitation and discrimination between certain Quranic sounds relies heavily on their durations, we have proposed and tested various strategies for modeling the Quranic sounds’ durations in order to increase the ability in distinguishing them properly and thus enhancing their overall recognition accuracy. Experiments have been carried out on a particular Quranic Corpus containing ten male speakers and more than eight hours of speech collected from recitations of the Holy Quran. The implemented system reached (99%) as average recognition rate; which reflects its robustness and performance.


  1. B. Jacob, M.M Sondhi and H.Yiteng, Springer Handbook of Speech Processing, Springer, (2008).
  2. Jurafsky, D.,Martin, J., Speech and Language Processing - An Introduction to Natural Language Processing, Computational Linguistics, and Speech Recognition, Prentice Hall, (2009).
  3. X. Huang, A. Acero and H.-W. Hon, Spoken Language Processing: a guide to theory, algorithm, and system development, Prentice Hall, (2001).
  4. M. A. Anusuya and S. Katti, Front end analysis of speech recognition: A review, Int. J. Speech Technology, vol. 14, no. 2, pp. 99–145, (2011).Bowman, M., Debray, S. K., and Peterson, L. L. (1993).
  5. Ahsiah, I., Noor, N. M., Idris, M. Y. I. Tajweed checking system to support recitation. International Conference on Advanced Computer Science and Information Systems (ICACSIS), pp.189–193. (2013).
  6. Noor Jamaliah Ibrahim, Zulkifli Mohd Yusoff, Zaidi Razak and Rosli Salleh, Improve Design for Automated Tajweed Checking Rules Engine of Quranic Verse Recitation: A Review. QURANICA-International Journal of Quranic Research, 1(1), pp. 39–50, (2011).
  7. Mohamed, S.A.E., et al. Virtual Learning System (Miqra’ah) for Quran Recitations for Sighted and Blind Students. Journal of Software Engineering and Applications, 7, 195-205, (2014).
  8. Yekache, Y., Kouninef, B., Mekelleche, Y., Mohamed, S., Building Quranic reader voice interface using sphinx toolkit. Journal of American Science, 9(11), pp. 473–479, (2013).
  9. Ding, W. and Marchionini, G. 1997 A Study on Video Browsing Strategies. Technical Report. University of Maryland at College Park.
  10. Y.O.M. Elhadj, “Preparation of speech database with perfect reading of the last part of the Holly Quran (in Arabic)”. Proc. of the 3rd IEEE International Conference on Arabic Language Processing (CITAL'09), pp. 5-8, May, (2009).
  11. Y.O.M. Elhadj, I.A. Alsughayeir, M. Alghamdi, M. Alkanhal, Y.M. Ohali, and A.M. Alansari. Computerized teaching of the Holy Quran (in Arabic), Final Technical Report, King Abdulaziz City for Sciences and Technology (KACST), Riyadh, KSA, (2012).
  12. Yahya O.M. ElHadj, Mansour Alghamdi, Mohammad Alkanhal , Phoneme-Based Recognizer to Assist Reading the Holy Quran. Recent Advances in Intelligent Informatics, Advances in Intelligent Systems and Computing .Springer. 235: 141-152., (2013).
  13. Yahya O.M. ElHadj, Mansour Alghamdi, Mohamed Alkanhal, Approach for Recognizing Allophonic Sounds of the Classical Arabic Based on Quran Recitations. Theory and Practice of Natural Computing, Lecture Notes in Computer Science. Springer. 8273: 57-67, (2013).
  14. Y.O.M. Elhadj, Mohamed .O.M. Khelifa, A. Yousfi and M. Belkasmi. “An Accurate Recognizer for Basic Arabic Sounds”. ARPN Journal of Engineering and Applied Sciences. vol. 11, no. 5, pp. 3239- 3243, Mar. (2016).
  15. Mohamed O.M. Khelifa, Y.O.M. Elhadj, Y. Abdellah and M. Belkasmi, “Enhancing Arabic Phoneme Recognizer using Duration Modeling Techniques,”, in proc. of Fourth International Conference on Advances in Computing, Electronics and Communication - ACEC 2016, Dec 15, 2016, Rome-Italy.
  16. Mohamed O.M. Khelifa, Y.O.M. Elhadj, Y. Abdellah and M. Belkasmi, “An Accurate HSMM-based System for Arabic phonemes Recognition,” in proc. of The IEEE Ninth International conference on Advanced Computational Intelligence (ICACI 2017), Feb. 2, 2017, Doha, Qatar.
  17. Mohamed O.M. Khelifa, Yousfi Abdellah, Yahya O.M. ElHadj and Mostafa Belkasmi, “Helpful Statistics in Recognizing Basic Arabic Phonemes” International Journal of Advanced Computer Science and Applications(ijacsa), 8(2), (2017).
  18. S.Young. HTK Book (V.3.4). Cambridge University Engineering. Department of Engineering, UK, (2009).


Quranic recitation, Quranic sounds, Classical Arabic Language, Hidden Markov models, Hidden semi-Markov Models, Duration modeling.