658 529
Full Length Article
Journal of Artificial Intelligence and Metaheuristics
Volume 1 , Issue 2, PP: 17-23 , 2022 | Cite this article as | XML | Html |PDF

Title

Automatic Speech Recognition for Qur’an Verses using Traditional Technique

  Hamzah A. Alsayadi 1 * ,   Mohammed Hadwan 2

1  Computer Science Department, Faculty of Sciences, Ibb University, Yemen
    (hamzah.sayadi@cis.asu.edu.eg)

2  Department of Information Technology, College of Computer, Qassim University, Buraydah 51452, Saudi Arabia ;Department of Computer Science, College of Applied Sciences, Taiz University, Taiz 6803, Yemen
    (m.hadwan@qu.edu.sa)


Doi   :   https://doi.org/10.54216/JAIM.010202

Received: March 11, 2022 Accepted: June 17, 2022

Abstract :

Deep learning is the one of approaches of machine learning that uses algorithms for building a model based on complex unstructured data. The Muslims Holy Qur’an book is written using Arabic diacritized text. In this paper, a traditional method to build a robust Qur’an versus recognition is proposed. The MFCC is used to extract features. These features are adapted using minimum phone error (MPE) as a discriminative model. The acoustic model was built using the deep neural network (DNN) model. We present an n-gram language model (LM). The dataset of Qur’an verses is used for training and evaluating the proposed model, consisting of 10 hours of .wav recitations performed by 60 reciters. The Experimental results showed that the proposed DNN model achieved a significantly low character error rate (CER) of 4.09% and a word error rate (WER) of 8.46%.

Keywords :

Quran verses; Deep neural network (DNN); Arabic ASR.

References :

[1] Geoffrey Khan, Michael P Streck, and Janet CE Watson. The Semitic languages: An international handbook,

volume 36. Walter de Gruyter, 2011.

[2] Hamzah A Alsayadi and Abeer M ElKorany. Integrating semantic features for enhancing arabic named entity

recognition. International Journal of Advanced Computer Science and Applications, 7(3), 2016.

[3] Norah Alsunaidi, Lobna Alzeer, Maha Alkatheiri, Alaa Habbabah, Marwah Alattas, Malak Aljabri, and Mona

Altassan. Abjad: Towards interactive learning approach to arabic reading based on speech recognition. Procedia

Computer Science, 142:198–205, 2018.

[4] Mohamed Hssini and Azzeddine Lazrek. Design of arabic diacritical marks. arXiv preprint arXiv:1107.4734,

2011.

[5] Hamzah A. Alsayadi, Abdelaziz A. Abdelhamid, Islam Hegazy, Bandar Alotaibi, and Zaki T. Fayed. Deep

investigation of the recent advances in dialectal arabic speech recognition. IEEE Access, 10:57063–

57079, 2022.

[6] JA Devenny. Arberry, aj,” the koran interpreted”(book review). Theological Studies, 17:440, 1956.

[7] Mohamed Abdelmonem Elsayed Khalil and Nor Hafzi Yusof. [the differences of the quranic qiraat in tafsir

imam al-tabari and its effects on the hukm of fqh] ikhtilaf al-qira’at al-qur’aniah f tafsir at-tabari wa asruhu ala alahkam

al-fqhiyyah: Dirasat tahliliah. Jurnal Islam Dan Masyarakat Kontemporari, 16(1):111–126, 2018.

[8] Ahmad Hanifuddin Ishaq and Ruston Nawawi. Ilmu tajwid dan implikasinya terhadap ilmu qira’ah. QAF,

1(1):15–37, 2017.

[9] Hamzah Alsayadi, Abdelaziz Abdelhamid, Islam Hegazy, and Zaki Taha. Data augmentation for arabic speech

recognition based on end-to-end deep learning. International Journal of Intelligent Computing and Information

Sciences, 21(2):50–64, 2021.

[10] Abdelaziz A Abdelhamid, Waleed H Abdulla, and Bruce A MacDonald. Roboasr: A dynamic speech

recognition system for service robots. In International Conference on Social Robotics, pages 485–495. Springer,

2012.

[11] Imad K Tantawi, Mohammad AM Abushariah, and Bassam H Hammo. A deep learning approach for automatic

speech recognition of the holy qur’an recitations. ¯ International Journal of Speech Technology, 24(4):1017–1032,

2021.

[12] Hassan Tabbal, W El Falou, and B Monla. Analysis and implementation of a” quranic” verses delimitation

system in audio fles using speech recognition techniques. In 2006 2nd international conference on information &

communication technologies, volume 2, pages 2979–2984. IEEE, 2006.

[13] Nazik O’mar Balula, Mohsen Rashwan, and Shrief Abdou. Automatic speech recognition (asr) systems for

learning arabic language and al-quran recitation: A review. 2021.

[14] Faza Thiraf and Dessi Puji Lestari. Hybrid hmm-blstm-based acoustic modeling for automatic speech

recognition on quran recitation. In 2018 International Conference on Asian Language Processing (IALP), pages

203–208. IEEE, 2018.

[15] Abdelaziz A Abdelhamid, Hamzah A Alsayadi, Islam Hegazy, and Zaki T Fayed. End-to-end arabic speech

recognition: A review. In Proceedings of the 19th Conference of Language Engineering (ESOLEC’19), Alexandria,

Egypt, pages 26–30, 2020.

[16] SR Shareef and YF Irhayim. A review: isolated arabic words recognition using artificial intelligent techniques.

In Journal of Physics: Conference Series, volume 1897, page 012026. IOP Publishing, 2021.

[17] Noor Jamaliah Ibrahim, Mohd Yamani Idna Idris, MY Zulkifli Mohd Yusoff, and Asma Anuar. The problems,

issues and future challenges of automatic speech recognition for quranic verse recitation: A review. Al-Bayan:

Journal of Qur’an and Hadith Studies, 13(2):168–196, 2015.

[18] Jawad H Alkhateeb. A machine learning approach for recognizing the holy quran reciter. International Journal

of Advanced Computer Science and Applications, 11(7), 2020.

[19] Khalid MO Nahar, M Ra’ed, A Moy’awiah, and M Malek. An efficient holy quran recitation recognizer based

on svm learning model. Jordanian Journal of Computers and Information Technology (JJCIT), 6(04), 2020.

[20] Mohammed Lataifeh, Ashraf Elnagar, Ismail Shahin, and Ali Bou Nassif. Arabic audio clips: Identification and

discrimination of authentic cantillations from imitations. Neurocomputing, 418:162–177, 2020.

[21] Mohammed Lataifeh and Ashraf Elnagar. Ar-dad: Arabic diversified audio dataset. Data in Brief, 33:106503,

2020.

[22] Ammar Mohammed, Mohd Shahrizal Bin Sunar, Md Salam, and Sah Hj. Recognition of holy quran recitation

rules using phoneme duration. In International Conference of Reliable Information and Communication Technology,

pages 343–352. Springer, 2017.

[23] Teddy Surya Gunawan, Nur Atikah Muhamat Saleh, and Mira Kartiwi. Development of quranic reciter

identification system using mfcc and gmm classifier. International Journal of Electrical & Computer

Engineering (2088-8708), 8(1), 2018.

[24] Ali M Alagrami and Maged M Eljazzar. Smartajweed automatic recognition of arabic quranic recitation rules.

arXiv preprint arXiv:2101.04200, 2020.

[25] Rehan Ullah Khan, A Qamar, and Mohammed Hadwan. Quranic reciter recognition: a machine learning

approach. Advances in Science, Technology and Engineering Systems Journal, 4(6):173–176, 2019.

[26] Hamzah A Alsayadi, Abdelaziz A Abdelhamid, Islam Hegazy, and Zaki T Fayed. Non-diacritized arabic speech

recognition based on cnn-lstm and attention-based models. Journal of Intelligent & Fuzzy Systems, (Preprint):1–13,

2021.

[27] Hamzah A Alsayadi, Abdelaziz A Abdelhamid, Islam Hegazy, and Zaki T Fayed. Arabic speech recognition

using end-to-end deep learning. IET Signal Processing, 15(8):521–534, 2021.

[28] David E Rumelhart, Geoffrey E Hinton, and Ronald J Williams. Learning representations by backpropagation

errors. nature, 323(6088):533–536, 1986.

[29] El-kenawy, El-Sayed M., Marwa M. Eid, and Abdelhameed Ibrahim. "Anemia estimation for covid-19 patients

using a machine learning model." Journal of Computer Science and Information Systems 17, no. 11 (2021):

2535-1451.

[30] Hussien, Hussien Rezk, El-Sayed M. El-Kenawy, and Ali I. El-Desouky. "EEG channel selection using a

modified grey wolf optimizer." European Journal of Electrical Engineering and Computer Science 5, no. 1

(2021): 17-24.

[31] Salamai, Abdullah Ali, El-Sayed M. El-kenawy, and Ibrahim Abdelhameed. "Dynamic voting classifier for risk

identification in supply chain 4.0." CMC-COMPUTERS MATERIALS & CONTINUA 69, no. 3 (2021): 3749-

3766.

[32] Eid, Marwa M., El-Sayed M. El-kenawy, and Abdelhameed Ibrahim. "A binary sine cosine-modified whale

optimization algorithm for feature selection." In 2021 National Computing Colleges Conference (NCCC), pp. 1-

6. IEEE, 2021.

[33] Eid, Marwa M., and M. El-Sayed. "El-kenawy, and Abdelhameed Ibrahim." An Advanced Patient Health

Monitoring System."." Journal of Computer Science and Information Systems 17.

[34] El-kenawy, El-Sayed M., Marwa M. Eid, and Abdelhameed Ibrahim. "Automatic identification from noisy

microscopic images." Journal of Computer Science and Information Systems 17, no. 11 (2021).

[35] Alharbi, Manal SF, and El-Sayed M. El-kenawy. "Optimize machine learning programming algorithms for

sentiment analysis in social media." International Journal of Computer Applications 174, no. 25 (2021): 38-43.

[36] Alharbi, Manal SF, and El-Sayed M. El-kenawy. "Recommendation System for Analyzing the Preference Data

of the Multimedia Software Tools in Education." (2021).

[37] El-kenawy, E. S. M. T. "Solar radiation machine learning production depend on training neural networks with

ant colony optimization algorithms." International Journal of Advanced Research in Computer and

Communication Engineering (IJARCCE) 7, no. 5 (2018): 1-4.

[38] El-Sayed Towfek, M. "El-kenawy. Trust Model for Dependable File Exchange in Cloud Computing."

International Journal of Computer Applications 180, no. 49 (2018): 22-27.

[39] El-Kenawy, El-Sayed M., Abdelhameed Ibrahim, Nadjem Bailek, Kada Bouchouicha, Muhammed A. Hassan,

Basharat Jamil, and Nadhir Al-Ansari. "Hybrid ensemble-learning approach for renewable energy resources

evaluation in Algeria." Computers, Materials & Continua 71, no. 3 (2022): 5837-5854.

[40] El-kenawy, El-Sayed M., Abdelhameed Ibrahim, Nadjem Bailek, Kada Bouchouicha, Muhammed A. Hassan,

Mehdi Jamei, and Nadhir Al-Ansari. "Sunshine duration measurements and predictions in Saharan Algeria

region: an improved ensemble learning approach." Theoretical and Applied Climatology 147, no. 3 (2022):

1015-1031.

[41] Ibrahim, Abdelhameed, Seyedali Mirjalili, Mohammed El-Said, Sherif SM Ghoneim, Mosleh M. Al-Harthi,

Tarek F. Ibrahim, and El-Sayed M. El-Kenawy. "Wind speed ensemble forecasting based on deep learning

using adaptive dynamic optimization algorithm." IEEE Access 9 (2021): 125787-125804.

[42] El-kenawy, El-Sayed M., Hattan F. Abutarboush, Ali Wagdy Mohamed, and Abdelhameed Ibrahim. "Advance

artificial intelligence technique for designing double T-shaped monopole antenna." CMC-COMPUTERS

MATERIALS & CONTINUA 69, no. 3 (2021): 2983-2995.


Cite this Article as :
Style #
MLA Hamzah A. Alsayadi, Mohammed Hadwan. "Automatic Speech Recognition for Qur’an Verses using Traditional Technique." Journal of Artificial Intelligence and Metaheuristics, Vol. 1, No. 2, 2022 ,PP. 17-23 (Doi   :  https://doi.org/10.54216/JAIM.010202)
APA Hamzah A. Alsayadi, Mohammed Hadwan. (2022). Automatic Speech Recognition for Qur’an Verses using Traditional Technique. Journal of Journal of Artificial Intelligence and Metaheuristics, 1 ( 2 ), 17-23 (Doi   :  https://doi.org/10.54216/JAIM.010202)
Chicago Hamzah A. Alsayadi, Mohammed Hadwan. "Automatic Speech Recognition for Qur’an Verses using Traditional Technique." Journal of Journal of Artificial Intelligence and Metaheuristics, 1 no. 2 (2022): 17-23 (Doi   :  https://doi.org/10.54216/JAIM.010202)
Harvard Hamzah A. Alsayadi, Mohammed Hadwan. (2022). Automatic Speech Recognition for Qur’an Verses using Traditional Technique. Journal of Journal of Artificial Intelligence and Metaheuristics, 1 ( 2 ), 17-23 (Doi   :  https://doi.org/10.54216/JAIM.010202)
Vancouver Hamzah A. Alsayadi, Mohammed Hadwan. Automatic Speech Recognition for Qur’an Verses using Traditional Technique. Journal of Journal of Artificial Intelligence and Metaheuristics, (2022); 1 ( 2 ): 17-23 (Doi   :  https://doi.org/10.54216/JAIM.010202)
IEEE Hamzah A. Alsayadi, Mohammed Hadwan, Automatic Speech Recognition for Qur’an Verses using Traditional Technique, Journal of Journal of Artificial Intelligence and Metaheuristics, Vol. 1 , No. 2 , (2022) : 17-23 (Doi   :  https://doi.org/10.54216/JAIM.010202)