Research Article
Deep Learning for Quranic Reciter Recognition and Audio Content Identification
@INPROCEEDINGS{10.1007/978-3-032-15154-4_10, author={Mohamet Tall and Thierno Ibrahima Diop and Ndeye Fatou Ngom and El hadj Abdoulaye Thiam}, title={Deep Learning for Quranic Reciter Recognition and Audio Content Identification}, proceedings={Innovations and Interdisciplinary Solutions for Underserved Areas. 8th International Conference, InterSol 2025, Ouagadougou, Burkina Faso, July 3--4, 2025, Proceedings}, proceedings_a={INTERSOL}, year={2026}, month={11}, keywords={Speaker identification Speaker recognition Deep learning Transfer learning Audio embedding}, doi={10.1007/978-3-032-15154-4_10} }- Mohamet Tall
Thierno Ibrahima Diop
Ndeye Fatou Ngom
El hadj Abdoulaye Thiam
Year: 2026
Deep Learning for Quranic Reciter Recognition and Audio Content Identification
INTERSOL
Springer
DOI: 10.1007/978-3-032-15154-4_10
Abstract
This paper presents a novel approach for identifying the reciter, sura, and verse of a given Quranic passage using pre-trained embedding models and transfer learning. Our approach involves training a deep learning model on a large Quranic recitation audio recordings dataset and using the resulting embeddings to compare and classify different reciters. We also present a workflow for identifying the specific sura and verse of a Quranic passage using a speech-to-text model and elastic-search for the query. We evaluate our approach using a variety of metrics and demonstrate its effectiveness in accurately identifying the reciter, sura, and verse of a given passage. We discuss the potential applications of this approach in the fields of Quranic studies and Islamic education and outline directions for future work in this area.