AN EFFICIENT HOLY QURAN RECITATION RECOGNIZER BASED ON SVM LEARNING MODEL

(Received: 29-Jun.-2020, Revised: 17-Aug.-2020 , Accepted: 6-Sep.-2020)

Authors Khalid M.O. Nahar, Ra'ed M. Al-Khatib, Moy'awiah A. Al-Shannaq, Malek M. Barhoush,

Keywords #Arabic language #Quran recitation #Artificial Neural Network #Support Vector Machine (SVM)

Abstract Holy Quran recitation recognition refers to the process of identifying the type of recitation, among those authorized styles of recitation (“Qira’ah” in Arabic). Several previous studies investigated the recitation rules (“Ahkam Al-Tajweed” in Arabic) that are applied by readers or reciters while reading the Holy Quran aloud, but no study has examined the problem of tracking the type of recitation used in the reading. Through this research, we can assist Holy Quran students to easily learn the perfect and accurate recitation by successfully applying Ahkam Al-Tajweed and help them distinguish between different recitations or "Qira’ah". In this paper, a recognition model is conducted to recognize the “Qira’ah” from the corresponding Holy Quran acoustic wave. This model was built upon three phases; the first phase is the Mel-Frequency Cepstrum Coefficients (MFCC) feature extraction of the acoustic signal and labeling it, the second phase is training Support Vector Machine (SVM) learning model the labeled features and finally, recognizing “Qira’ah” based on this trained model. To attain this, we have built our corpus, which has 10 categories, each of which is labeled as one type of Holy Quran recitation or “Qira’ah”. Different machine learning algorithms were applied and compared. Experimental results proved the superiority of our proposed SVM-based recognition model for “Qira’ah” over other machine learning algorithms with a success rate of 96%.

References

[1] Y. Kato, "Speech Recognition System," Journal of the Acoustical Society of America, vol. 107, no. 5, p. 2326, 2000.

[2] L. R. Rabiner and B.-H. Juang, Fundamentals of Speech Recognition, PTR Prentice Hall Englewood Cliffs, 1993.

[3] T. K. Das and K. M. O. Nahar, "A Voice Identification System Using Hidden Markov Model," Indian Journal of Science and Technology, vol. 9, no. 4, DOI: 10.17485/ijst/2016/v9i4/83894, 2016.

[4] J. M. Baker et al., "Developments and Directions in Speech Recognition and Understanding, Part 1 [DSP Education]," IEEE Signal Processing Magazine, vol. 26, no. 3, pp. 75-80, 2009.

[5] J. Chong, E. Gonina, D. Kolossa, S. Zeiler and K. Keutzer, "An Automatic Speech Recognition Application Framework for Highly Parallel Implementations on the GPU," Technical Report No. UCB/EECS-2012-47, [Online], available: http://www.eecs.berkeley.edu/Pubs/TechRpts/2012/EECS-2012-47.html, 2012.

[6] K. Nahar, H. Al-Muhtaseb, W. Al-Khatib, M. Elshafei and M. Alghamdi, "Arabic Phonemes Transcription Using Data Driven Approach," Int. Arab J. of Information Technology, vol. 12, no. 3, pp. 237–245, 2015.

[7] Y.-H. Shao and N.-Y. Deng, "A Coordinate Descent Margin Based-twin Support Vector Machine for Classification," Neural Networks, vol. 25, pp. 114–121, 2012.

[8] K. Nahar, M. Abu Shquier, W. G. Al-Khatib, H. Al-Muhtaseb and M. Elshafei, "Arabic Phonemes Recognition Using Hybrid LVQ/HMM Model for Continuous Speech Recognition," International Journal of Speech Technology, vol. 19, no. 3, pp. 495–508, 2016.

[9] J. Allen, D. Byron, M. Dzikovska, G. Ferguson, L. Galescu and A. Stent, "An Architecture for a Generic Dialogue Shell," Natural Language Engineering, vol. 6, no. 3–4, pp. 213–228, 2000.

[10] K. M. O. Nahar, W. G. Al-Khatib, M. Elshafei, H. Al-Muhtaseb and M. M. Alghamdi, "Data-driven Arabic Phoneme Recognition Using Varying Number of HMM States," Proc. of the 1st IEE International Conference on Communications, Signal Processing and Their Applications (ICCSPA 2013), pp. 1–6, Sharjah, United Arab Emirates, 2013.

[11] M. Abdul-Mageed, M. Diab and S. Kübler, "SAMAR: Subjectivity and Sentiment Analysis for Arabic Social Media," Computer Speech and Language, vol. 28, no. 1, pp. 20–37, 2014.

[12] F. Diehl, M. J. F. Gales, M. Tomalin and P. C. Woodland, "Morphological Decomposition in Arabic ASR Systems," Computer Speech and Language, vol. 26, no. 4, pp. 229–243, 2012.

[13] K. Kirchhoff et al., "Novel Approaches to Arabic Speech Recognition: Report from the 2002 Johns-Hopkins Summer Workshop," Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP’03), vol. 1, pp. I344-I347, Hong Kong, China, 2003.

[14] E. Zarrouk, Y. Ben Ayed and F. Gargouri, "Hybrid Continuous Speech Recognition Systems by HMM, MLP and SVM: A Comparative Study," International Journal of Speech Technology, vol. 17, no. 3, pp. 223–233, 2014.

[15] A. Al-Otaibi, "Speech Processing," Britsh Library in Association with UMI, 1988.

[16] M. M. El Choubassi, H. E. El Khoury, C. E. J. Alagha, J. A. Skaf and M. A. Al-Alaoui, "Arabic Speech Recognition Using Recurrent Neural Networks," Proceedings of the 3rd IEEE International Symposium on Signal Processing and Information Technology (IEEE Cat. No.03EX795) (ISSPIT 2003), pp. 543–547, Darmstadt, Germany, 2003.

[17] A. Ali et al., "Automatic Dialect Detection in Arabic Broadcast Speech," arXiv Paper, arXiv1509.06928, [Online], available: https://arxiv.org/pdf/1509.06928.pdf, 2015.

[18] F. Biadsy, N. Habash and J. Hirschberg, "Improving the Arabic Pronunciation Dictionary for Phone and Word Recognition with Linguistically-based Pronunciation Rules," Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics, pp. 397–405, United States, 2009.

[19] N. J. Ibrahim, M. Y. Zulkifli and H. Mohd, "Improved Design for Automated Tajweed Checking Rules Engine of Quranic Verses Recitation : A Review," International Journal on Quranic Research, no. January, pp. 39–50, 2011.

[20] A. Muhammad, Z. Ul Qayyum, M. Waqar Mirza, S. Tanveer, A. M. Martinez-Enriquez and A. Z. Syed, "E-hafiz: Intelligent System to Help Muslims in Recitation and Memorization of Quran," Life Science Journal, vol. 9, no. 1, pp. 534–541, 2012.

[21] T. Mssraty and Q. Faryadi, "Teaching the Qur’anic Recitation with Harakatt: A Multimedia-based Interactive Learning Method," International Journal of Scientific and Enginerring Research, vol. 3, no. 8, pp. 1–4, 2012.

[22] N. W. Arshad et al., "Makhraj Recognition for Al-Quran Recitation Using MFCC," International Journal of Intelligent Information Processing, vol. 4, no. 2, pp. 45–53, Jun. 2013.

[23] T. Sabbah and A. Selamat, "Support Vector Machine-based Approach for Quranic Words Detection in Online Textual Content," Proc. of the 8th Malaysian Software Engineering Conference (MySEC 2014), pp. 325–330, Langkawi, Malaysia, 2014.

[24] S. A. E. Mohamed, A. S. Hassanin and M. T. Ben Othman, "Virtual Learning System (Miqra’ah) for Quran Recitations for Sighted and Blind Students," Journal of Software Engineering and Applications, vol. 07, no. 04, pp. 195–205, 2014.

[25] Y. O. M. Elhadj, M. Alghamdi and M. Alkanhal, "Phoneme-based Recognizer to Assist Reading the Holy Quran," Recent Advances in Intelligent Informatics, Part of the Advances in Intelligent Systems and Computing Book Series (AISC), vol. 235, pp. 141–152, 2014.

[26] H. M. A. Tabbaa and B. Soudan, "Computer-aided Training for Quranic Recitation," Procedia-Social and Behavioral Sciences, vol. 192, pp. 778–787, 2015.

[27] M. Y. El Amrani, M. M. H. Rahman, M. R. Wahiddin and A. Shah, "Building CMU Sphinx Language Model for the Holy Quran Using Simplified Arabic Phonemes," Egyptian Informatics Journal, vol. 17, no. 3, pp. 305–314, 2016.

[28] M. Al-Ayyoub, N. A. Damer and I. Hmeidi, "Using Deep Learning for Automatically Determining Correct Application of Basic Quranic Recitation Rules," International Arab Journal of Information Technology, vol. 15, no. 3, pp. 620–625, 2018.

[29] M. Y. El Amrani, M. R. Wahiddin, M. M. H. Rahman and A. Shah, "Towards Using CMU Sphinx Tools for the Holy Quran Recitation Verification," International Journal on Islamic Applications in Computer Science and Technol., vol. 4, no. 2, pp. 10–15, 2016.

[30] A. Hughes, P. Trudgill and D. Watt, English Accents and Dialects: An Introduction to Social and Regional Varieties of English in the British Isles, Routledge, 2013.

[31] O. F. Zaidan and C. Callison-Burch, "Arabic Dialect Identification," Computational Linguistics, vol. 40, no. 1, pp. 171–202, 2014.

[32] H. K. Tayyeh, M. S. Mahdi and A. S. A. Al-Jumaili, "Novel Steganography Scheme Using Arabic Text Features in Holy Quran," International Journal of Electrical and Computer Engineering, vol. 9, no. 3, pp. 1910-1918, 2019.

[33] L. C. Moore, "Learning by Heart in Qur’anic and Public Schools in Northern Cameroon," Social Analysis, vol. 50, no. 3, pp. 109–126, 2006.

[34] A. Rasmussen, Women, the Recited Qur’an and Islamic Music in Indonesia, Univ. of California Press, 2010.

[35] Z. A. Adhoni, H. Al Hamad, A. A. Siddiqi, M. Parvez and Z. A. Adhoni, "Cloud-based Online Portal and Mobile Friendly Application for the Holy Qur’an," Life Science Journal, vol. 10, no. 12, 2013.

[36] M. M. Khan, The Translation of the Meanings of Sahih Al-Bukhâri, vol. 5, Kazi Publications, 1997.

[37] T. Wahbi and A. Gadeed, "The Recognition of Holy Quran Reading Types ‘Rewaih," International Journal of Advanced Research in Computer Science, vol. 5, no. 3, pp. 37-40, 2014.

[38] A. A. Chaudhari, "Effect of Varying MFCC Filters for Speaker Recognition," International Journal of Computer applications, vol. 128, no. 14, pp. 7–9, 2015.

[39] M. N. Al-Kabi, G. Kanaan, R. Al-Shalabi, M. O. K. Nahar and M. B. Bani-Ismail, "Statistical Classifier of the Holy Quran Verses (Fatiha and Yaseen Chapters)," Journal of Applied Sciences, vol. 5, no. 3, pp. 580–583, 2005.

[40] H. Okuyucu, A. Kurt and E. Arcaklioglu, "Artificial Neural Network Application to the Friction Stir Welding of Aluminum Plates," Materials and Design Journal, vol. 28, no. 1, pp. 78–84, 2007.

[41] M. Al-Abri and N. Hilal, "Artificial Neural Network Simulation of Combined Humic Substance Coagulation and Membrane Filtration," Chemical Engineering Journal, vol. 141, no. 1, pp. 27–34, 2008.

[42] J. Huang, J. Lu and C. X. Ling, "Comparing Naive Bayes, Decision Trees and SVM with AUC and Accuracy," Proc. of the 3rd IEEE International Conference on Data Mining (ICDM 2003), pp. 553–556, Melbourne, FL, USA, 2003.

[43] A. Statnikov, C. F. Aliferis, D. P. Hardin and I. Guyon, A Gentle Introduction to Support Vector Machines in Biomedicine: Volume 2: Case Studies and Benchmarks, World Scientific, 2013.

[44] J. Platt et al., "Probabilistic Outputs for Support Vector Machines and Comparisons to Regularized Likelihood Methods," Adv. Large Margin Classif., vol. 10, no. 3, pp. 61–74, 1999.

[45] Z.-Q. Zeng, H.-B. Yu, H.-R. Xu, Y.-Q. Xie and J. Gao, "Fast Training Support Vector Machines Using Parallel Sequential Minimal Optimization," Proc. of the 3rd International Conference on Intelligent System and Knowledge Engineering, vol. 1, pp. 997–1001, Xiamen, China, 2008.

[46] L. Wang, Support Vector Machines: Theory and Applications, vol. 177, Springer Science & Business Media, 2005.

[47] S. Tong and E. Chang, "Support Vector Machine Active Learning for Image Retrieval," Proceedings of the 9th ACM International Conference on Multimedia, pp. 107–118, [Online], available: https://doi.org/10.1145/500141.500159, 2001.

[48] M. A. Hearst et al., "Support Vector Machines," IEEE Intelligent Systems, vol. 13, no. 4, pp. 18–28, 1998.

[49] R. Collobert and S. Bengio, "SVMTorch: Support Vector Machines for Large-scale Regression Problems," Journal of Machine Learning Research, vol. 1, no. Feb, pp. 143–160, 2001.

[50] C. J. C. Burges, "A Tutorial on Support Vector Machines for Pattern Recognition," Data Mining and Knowledge Discovery, vol. 2, no. 2, pp. 121–167, 1998.

[51] M. Lalaoui, A. El Afia and R. Chiheb, "A self-tuned Simulated Annealing Algorithm Using Hidden Markov Model," International Journal of Electrical and Computer Engineering, vol. 8, no. 1, p. 291, 2018.

[52] A. El Afia, M. Sarhani and O. Aoun, "Hidden Markov Model Control of Inertia Weight Adaptation for Particle Swarm Optimization," IFAC-PapersOnLine, vol. 50, no. 1, pp. 9997–10002, 2017.

[53] W. M. Campbell, J. P. Campbell, D. A. Reynolds, E. Singer and P. A. Torres-Carrasquillo, "Support Vector Machines for Speaker and Language Recognition," Computer Speech and Language, vol. 20, no. 2, pp. 210–229, 2006.

[54] K. M. O. Nahar, M. Al-Shannaq, A. Manasrah, R. Alshorman and I. Alazzam, "A Holy Quran Reader/Reciter Identification System Using Support Vector Machine," International Journal of Machine Learning and Computing, vol. 9, no. 4, pp. 458-464, 2019.

[55] U. Shrawankar, "Techniques for Feature Extraction in Speech Recognition System: A Comparative Study," arXiv Prepr, arXiv1305.1145, [Online], Available: https://arxiv.org/ftp/arxiv/papers/1305/1305.1145.pdf, 2013.

[56] T. Kinnunen, R. Saeidi, J. Sandberg and M. Hansson-Sandsten, "What Else Is New Than the Hamming Window? Robust MFCCs for Speaker Recognition via Multitapering," Proc. of the 11th Annual Conference of the International Speech Communication Association, Japan, [Online], Available: https://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.178.5674&rep=rep1&type=pdf, 2010.

[57] S. Chowdhury, N. Mamun, A. A. S. Khan and F. Ahmed, "Text Dependent and Independent Speaker Recognition Using Neural Responses from the Model of the Auditory System," Proc. of the IEEE International Conference on Electrical, Computer and Communication Engineering (ECCE), pp. 871–874, Cox's Bazar, Bangladesh, 2017.

[58] R. Bharti and P. Bansal, "Real Time Speaker Recognition System Using MFCC and Vector Quantization Technique," International Journal of Computer Applications, vol. 117, no. 1, pp. 25-31, 2015.

[59] S. A. Majeed, H. Husain, S. A. Samad and T. F. Idbeaa, "Mel Frequency Cepstral Coefficients (MFCC) Feature Extraction Enhancement in the Application of Speech Recognition: A Comparison Study," Journal of Theoretical and Applied Information Technology, vol. 79, no. 1, pp. 38–56, 2015.