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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) International Journal of Advances in Intelligent Informatics
Alshargabi, Asma
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Computer Science & IT Engineering

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Techniques of Quran reciters recognition: a review Alomari, Ibrahim; Alshargabi, Asma; Hadwan, Mohammed
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i3.pp1683-1695

Abstract

The Quran is the holy book of the Islam. Reading and listening to the Quran is an important part of the daily life of Muslims. Muslims are keen to listen to recitations of Quran by skilled reciters to learn the correct recitation for the purpose of understanding and contemplating. Therefore, there are large variety of audio recitations for many skilled reciters. With the availability of this huge amount of recitations and also with the great progress in voice recognition technologies, many research efforts have been devoted to contribute making recitation better using artificial intelligence. One useful application in this area is identifying the reciters of the Quran. There are various solutions introduced by researchers; however, these solutions vary significantly in terms of accuracy, and efficiency. This research seeks to provide a review of these solutions. It also reviews available datasets using different criteria. Finally, some open issues and challenges were addressed.
Towards a high-accuracy framework for quranic reciter recognition using deep learning and a large-scale benchmark dataset Al-Omari, Ibrahim; Alshargabi, Asma
International Journal of Advances in Intelligent Informatics Vol 12, No 1 (2026): February 2026
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v12i1.2288

Abstract

Speaker recognition aims to identify who is speaking from their voice and is widely used in security, personalization, and archival search. A related, culturally significant task is recognizing Qur’ān reciters from their recitations. The Quran is the central religious text of Islam and is recited with codified pronunciation and melodic rules (tajwīd and maqām). Distinguishing reciters can support digital archiving, educational feedback, and retrieval of stylistically similar recitations. We present a controlled comparison of deep learning approaches for Qur’ān reciter recognition, contrasting feature-based pipelines with end-to-end waveform models under a unified protocol. Using ṣūrah Al-Tawbah recitations from 12 reciters (18,540 clips; fixed 2 s segments), an X-Vector architecture with Mel-Frequency Cepstral Coefficients (MFCCs) attains perfect test performance (accuracy/precision/recall/F1 =100%). Convolutional Neural Network (CNN) and Bidirectional LSTM (BLSTM) baselines achieve near-optimal results (99.96% accuracy and F1), while an end-to-end X-Vector trained on raw waveforms reaches 98.77% accuracy (F1 = 0.9877). These findings indicate that explicit spectral features remain advantageous for short segments requiring fine acoustic discrimination, although end-to-end learning is competitive and simplifies preprocessing. We release the curated dataset with standardized splits and training scripts to enable reproducible benchmarking. Overall, feature-informed X-Vectors constitute a strong reference for short-segment reciter identification, and our results motivate hybrid/self-supervised front ends, tajwīd-aware analysis, and real-time, on-device deployment.
Co-Authors Al-Omari, Ibrahim Alomari, Ibrahim Hadwan, Mohammed