Recitation of The Holy Quran Verses Recognition System Based on Speech Recognition Techniques
Abstract
Abstract Views: 0
Arabic is the language in which the Holy Quran was revealed to Mohammed (S.A.W). Muslims claim that the Holy Quran has not been tampered with since it has been preserved. The Arabic Quran should be read exactly as it has been written. With the flourishment of Islam and the appearance of faults in Quran’s recitation, the experts created Tajweed to preserve Allah's revelation. The Holy Quran's authenticity and purity must be protected from erasure or contamination. The current study examined speech recognition techniques used in the Quran’s recitation along with their strengths and faults. Moreover, it also examined the Quranic text verification paradigm. The development of a computer-aided system, to automatically learn the Holy Quran's recitation, is a practical learning technique. Computer-aided Programming Language (CAPL) has gained popularity in recent years. Moreover, numerous researches have been conducted so far to improve these methods, especially in second-language instruction. Computer technologies can help language teachers with pronunciation and accent reduction. The computers play an essential role in automated tutoring. With the help of computer, words can be learned at home. CAPL's strict application is to automate the Holy Quran’s recitation unlike a language-learning exercise, where many pronunciations may be appropriate. There is minimal opportunity for variation while reciting the Holy Quran in Arabic language. The current study presented a concept for Quran’s recitation verification system along with an overview of Quran’s voice recognition techniques.
Downloads
References
A. A. Abdullah, M. D. Awang, and N. Abdullah, “Islamic tourism: The characteristics, concept and principles,” KnE Soc. Sci., Jul. 2020, pp. 196–215, doi: https://doi.org/10.18502/kss.v4i9.7326
R. Hakim, M. Ritonga, K. Khodijah, Z. Zulmuqim, R. Remiswal, and A. R. Jamalyar, “Learning strategies for reading and writing the quran: improving student competence as preservice teachers at the faculty of tarbiyah and teacher training,” Educ. Res. Int., vol. 2022, Art. no. 3464265, June 2022, doi:
https://doi.org/10.1155/2022/3464265
I. K. Tantawi, M. A. M. Abushariah, and B. H. Hammo, “A deep learning approach for automatic speech recognition of The Holy Qur’ān recitations,” Int. J. Speech Technol., vol. 24, no. 4, pp. 1017–1032, Dec. 2021, doi: https://doi.org/ 10.1007/s10772-021-09853-9
A. Ali, S. Naeem, S. Anam, and M. M. Ahmed, “Current state of artificial intelligence (AI) in oncology: A review,” Curr. Trends OMICS, vol. 3, no. 1, pp. 1–17, June 2023, doi: https://doi.org/10.32350/cto.31.01
M. Zubair, “Machine learning based biomedical image analysis and feature extraction methods,” J. Appl. Emerg. Sci., vol. 13, no. 1, pp. 31–39, June 2023, doi: http://dx.doi.org/10. 36785/jaes.131551
A. Ali et al., “Connecting Arabs: bridging the gap in dialectal speech recognition,” Commun. ACM, vol. 64, no. 4, pp. 124–129, Apr. 2021, doi: https://doi.org/10.1145/3451150
J. Younes, E. Souissi, H. Achour, and A. Ferchichi, “Language resources for Maghrebi Arabic dialects’ NLP: A survey,” Lang. Resour. Eval., vol. 54, no. 4, pp. 1079–1142, Dec. 2020, doi: https://doi.org/10.1007/s10579-020-09490-9
A. O. Salau, T. D. Olowoyo, and S. O. Akinola, “Accent classification of the three major nigerian indigenous languages using 1D CNN LSTM network model,” in Advances in Computational Intelligence Techniques, S. Jain, M. Sood, and S. Paul, Eds., Singapore: Springer, 2020, pp. 1–16, doi: https://doi.org/10. 1007/978-981-15-2620-6_1
I. Albayrak, “Revisiting the meaning of the divine preservation of the Qur’an: With special references to verse 15: 9,” Religions, vol. 13, no. 11, Art. no.1064, Nov. 2022, doi: https://doi.org/10.3390/rel13111064
G. Krishna, C. Tran, M. Carnahan, Y. Han, and A. H. Tewfik, “Generating EEG features from acoustic features,” in 28th Eur. Signal Process. Conf., Jan. 2021, pp. 1100–1104, doi: https://doi.org/10.23919/Eusipco47968.2020.9287498
A. Ali, S. Anam, and M. M. Ahmed, “Shannon entropy in artificial intelligence and its applications based on information theory,” J. Appl. Emerg. Sci., vol. 13, no. 1, pp. 9–17, June 2023, doi: http://dx.doi.org/ 10.36785/jaes.131549
S. Naeem, “Network security and cryptography challenges and trends on recent technologies,” J. Appl. Emerg. Sci., vol. 13, no. 1, pp. 1–8, June 2023, doi: http://dx.doi.org/10.36785/jaes.131546
Y. Wang et al., “Transformer-based acoustic modeling for hybrid speech recognition,” in ICASSP 2020-2020 IEEE Int. Conf. Acoustics, Speech Signal Proc., May 2020, pp. 6874–6878, doi: https://doi.org/10. 1109/ICASSP40776.2020.9054345
S. Naeem and A. Ali, “Bees algorithm based solution of non-convex dynamic power dispatch issues in thermal units,” J. Appl. Emerg. Sci., vol. 12, no. 1, June 2022, doi: http://dx.doi.org/10.36785/jaes.121540
A. Ali and S. Naeem, “The controller parameter optimization for nonlinear systems using particle swarm optimization and genetic algorithm,” J. Appl. Emerg. Sci., vol. 12, no. 1, June 2022, doi: http://dx.doi.org/10. 36785/jaes.121538
A. Ali, S. Naeem, S. Anam, and M. M. Ahmed, “Machine learning methods of IoT security and future application,” Proc. Pak. Acad. Sci. Phys. Comput. Sci., vol. 59, no. 3, pp. 1–16, Aug. 2022, doi: https://doi.org/10. 53560/PPASA(59-3)782
N. J. Ibrahim, Z. M. Yusoff, and Z. Razak, “Improve design for automated Tajweed checking rules engine of Quranic verse recitation: a review,” Int. J. Quranic Res., vol. 1, no. 1, pp. 39–50, 2011.
M. S. Abdo, A. H. Kandil, A. M. El-Bialy, and S. A. Fawzy, “Automatic detection for some common pronunciation mistakes applied to chosen Quran sounds,” in 5th Cairo Int. Biomed. Eng. Conf., IEEE, Dec. 2010, pp. 219–222, doi: https://doi. org/10.1109/CIBEC.2010.5716073
S. Naeem, A. Ali, S. Anam, and M. M. Ahmed, “An unsupervised machine learning algorithms: Comprehensive review,” Int. J. Comput. Digit. Syst., vol. 13, no. 1, Jul. 2023, doi: http://dx.doi.org/10.12785/ijcds/130172
A. Ali, S. Naeem, S. Anam, and M. M. Ahmed, “A state of art survey for big data processing and NoSQL database architecture,” Int. J. Comput. Digit. Syst., vol. 4, no. 1, July 2023, doi: http://dx.doi.org/10.12785/ijcds/140124
P. S. P. Kumar, G. T. Yadava, and H. S. Jayanna, “Continuous Kannada speech recognition system under degraded condition,” Circuits Syst. Sig. Process., vol. 39, no. 1, pp. 391–419, Jan. 2020, doi: https://doi.org/ 10.1007/s00034-019-01189-9
S. A. Bukhari, A. Ali, and S. Naeem, “Promoting the leadership effectiveness in HEC organizations of southern Punjab, Pakistan,” UMT Educ. Rev., vol. 5, no. 2, pp. 102–119, Dec. 2022, doi: https://doi.org/10.32350/uer.52.06
A. Ali, S. Naeem, S. Anam, and M. M. Ahmed, “Entropy in information theory from many perspectives and various mathematical models,” J. Appl. Emerg. Sci., vol. 12, no. 2, pp. 156–165, Dec. 2022, doi: http://dx.doi. org/10.36785/jaes.122548
A. Ali, S. Naeem, S. Anam, and M. M. Ahmed, “Machine learning for intrusion detection in cyber security: Applications, challenges, and recommendations,” UMT Artif. Intell. Rev., vol. 2, no. 2, pp. 41–64, Dec. 2022, doi: https://doi.org/10.32350/icr.0202.03
K. C. Yoong and K. S. Hong, “Malaysian English large vocabulary continuous speech recognizer: an improvement using acoustic model adaptation”, IEM J., pp. 53–61.
K.-F. Lee and H.-W. Hon, “Speaker-independent phone recognition using hidden Markov models,” IEEE Trans. Acoust. Speech Signal Process., vol. 37, no. 11, pp. 1641–1648, Nov. 1989, doi: https://doi.org/10.1109/29.46546
H. Prakoso, R. Ferdiana, and R. Hartanto, “Indonesian automatic speech recognition system using CMU Sphinx toolkit and limited dataset,” in Int. Symp. Elect. Smart. Devic, IEEE, 2016, pp. 283–286, doi: https://doi.org/10.1109/ISESD.2016.7886734
S. K. Saksamudre, P. P. Shrishrimal, and R. R. Deshmukh, “A review on different approaches for speech recognition system,” Int. J. Comput. Appl., vol. 115, no. 22, pp. 23–28, Apr. 2015,
G. Luo, P. Yang, M. Chen, and P. Li, “HCI on the table: Robust gesture recognition using acoustic sensing in your hand,” IEEE Access, vol. 8, pp. 31481–31498, Feb. 2020, doi: https://doi.org/10.1109/ACCESS.2020.2973305
M. Zubair, “A brief history of information theory by Claude Shannon in data communication,” J. Appl. Emerg. Sci., vol. 13, no. 1, pp. 23–30, June 2023, doi: http://dx.doi.org/ 10.36785/jaes.131550
A. A. Kabir, “Memorizing the sacred in the digital age: Exploring Qur’an memorization experiences using physical & digital formats,” Master thesis, Univ. Maryland, College Park, Maryland, USA, 2021.
M. M. T. Nur, S. S. Dola, A. K. Banik, T. Akhter, and N. Hossain, “Voice recognition using machine learning and central database to enhance security system,” Graduation thesis, Depart. Comput. Sci. Eng., Brac Univ., Dhaka, Bangladesh, 2022.
A. Ali, B. N. Hashmi, A. Batool, S. Naeem, S. Anam, and M. M. Ahmed, “Machine learning based implementation of home automation using smart mirror,” UMT Artif. Intell. Rev., vol. 2, no. 1, Art. no. 1, Mar. 2022, doi: https://doi.org/10.32350/ Umtair.21.002
H. Strik, K. Truong, F. de Wet, and C. Cucchiarini, “Comparing different approaches for automatic pronunciation error detection,” Speech Commun., vol. 51, no. 10, pp. 845–852, Oct. 2009, https://doi.org/10.1016/j.specom.2009.05.007
A. Al Hindi, M. Alsulaiman, G. Muhammad, and S Al-Kahtani, “Automatic pronunciation error detection of nonnative Arabic Speech," in 11th Int. Conf. Comput. Syst. Appli., 2023, pp. 190–197, doi: https://doi.org/10.1109/AICCSA.2014.7073198
S. M. Witt and S. J. Young, “Phone-level pronunciation scoring and assessment for interactive language learning,” Speech Commun., vol. 30, no. 2, pp. 95–108, Feb. 2000, doi: https://doi.org/10.1016/S0167-6393(99)00044-8
C. Cucchiarini, F. D. Wet, H. Strik, and L. Boves, “Assessment of dutch pronunciation by means of automatic speech recognition technology,” in 5th Int. Conf. Spoken Lang. Proc., 1998, Art. no. 0751, doi: https://doi.org/10.21437/ICSLP.1998-720
M. Maqsood, H. A. Habib, S. M. Anwar, M. A. Ghazanfar, and T. Nawaz, “A comparative study of classifier based mispronunciation detection system for confusing,” Nucleus, vol. 54, no. 2, pp. 114–120, June 2017.
M. Maqsood, “Selection of discriminative features for Arabic phoneme’s mispronunciation detection,” Pak. J. Sci., vol. 67, no. 4, pp. 405–412, Dec. 2015, doi: https://doi.org/10.57041/pjs.v67i4.607
UMT-AIR follow an open-access publishing policy and full text of all published articles is available free, immediately upon publication of an issue. The journal’s contents are published and distributed under the terms of the Creative Commons Attribution 4.0 International (CC-BY 4.0) license. Thus, the work submitted to the journal implies that it is original, unpublished work of the authors (neither published previously nor accepted/under consideration for publication elsewhere). On acceptance of a manuscript for publication, a corresponding author on the behalf of all co-authors of the manuscript will sign and submit a completed the Copyright and Author Consent Form.
