Monitoring the memorization of the holy Qur'an based on speech recognition and NLP techniques

Shaklawoon, Omar Saleh and Shafter, Ali Salem and Abuzaraida, Mustafa Ali and Zeki, Akram M. and Attarbashi, Zainab (2024) Monitoring the memorization of the holy Qur'an based on speech recognition and NLP techniques. In: Artificial Intelligence in Sharia and Legal Sciences, Ait Meloul, Morocco.

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Abstract

The development of artificial intelligence technologies, such as speech recognition technology, has accelerated in recent decades. Applications that rely on speech recognition technology, such as voice assistants, have also accelerated, and these applications reduce job completion time and effort. This technology relies on its work on the handling of natural language processing (NLP) and neural networks. In this study, we used a model based on Hidden Markov Model (HMM), which is one of the most well-known model which used in speech recognition approaches. Based on that, the user can recite the Qur'an on the proposed system that helps anyone who wants to review the memorization of the Qur'an. The system will give an alert in case of making any mistake in the order of the Verses, and help to know the next Verse by showing it in text. The process is done by the speech recognition system through recognizing the speech-to-text and comparing it with text in the database. The results show that, the system achieved an accuracy rate of 96%.

Item Type:	Proceeding Paper (Plenary Papers)
Uncontrolled Keywords:	Speech Recognition Natural Language Processing
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science Q Science > QA Mathematics > QA76 Computer software
Kulliyyahs/Centres/Divisions/Institutes (Can select more than one option. Press CONTROL button):	Kulliyyah of Information and Communication Technology Kulliyyah of Information and Communication Technology
Depositing User:	Dr Zainab Senan Mahmod
Date Deposited:	26 Jul 2024 09:15
Last Modified:	26 Jul 2024 11:15
URI:	http://irep.iium.edu.my/id/eprint/113297

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