Towards a Unified Benchmark for Arabic Pronunciation Assessment: Qur'anic Recitation as Case Study
El Kheir, Yassine Ibrahim, Omnia Meghanani, Amit Almarwani, Nada Toyin, Hawau Olamide Alharbi, Sadeen Alfadly, Modar Alkanhal, Lamya Selim, Ibrahim Elbatal, Shehab
El Kheir, Yassine
Ibrahim, Omnia
Meghanani, Amit
Almarwani, Nada
Toyin, Hawau Olamide
Alharbi, Sadeen
Alfadly, Modar
Alkanhal, Lamya
Selim, Ibrahim
Elbatal, Shehab
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Department
Natural Language Processing
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Type
Conference proceeding
Date
2025
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Language
English
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Abstract
We present a unified benchmark for mispronunciation detection in Modern Standard Arabic (MSA) using Qur'anic recitation as a case study. Our approach lays the groundwork for advancing Arabic pronunciation assessment by providing a comprehensive pipeline that spans data processing, the development of a specialized phoneme set tailored to the nuances of MSA pronunciation, and the creation of the first publicly available test set for this task, which we term as the Qur'anic Mispronunciation Benchmark (QuranMB.v1). Furthermore, we evaluate several baseline models to provide initial performance insights, thereby highlighting both the promise and the challenges inherent in assessing MSA pronunciation. By establishing this standardized framework, we aim to foster further research and development in pronunciation assessment in Arabic language technology and related applications. All models and datasets are available at: https://huggingface.co/IqraEval.
Citation
Y. El Kheir et al., “Towards a Unified Benchmark for Arabic Pronunciation Assessment: Qur’anic Recitation as Case Study,” pp. 2410–2414, Jun. 2025, doi: 10.21437/INTERSPEECH.2025-1497
Source
Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH
Conference
26th Interspeech Conference 2025
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Source
26th Interspeech Conference 2025
Publisher
International Speech Communication Association