Towards a Unified Benchmark for Arabic Pronunciation Assessment: Quranic Recitation as Case Study

Yassine El Kheir; Omnia Ibrahim; Amit Meghanani; Nada Almarwani; Hawau Olamide Toyin; Sadeen Alharbi; Modar Alfadly; Lamya Alkanhal; Ibrahim Selim; Shehab Elbatal; Salima Mdhaffar; Thomas Hain; Yasser Hifny; Mostafa Shahin; Ahmed Ali

arXiv · 2506.07722

Towards a Unified Benchmark for Arabic Pronunciation Assessment: Quranic Recitation as Case Study

Yassine El Kheir·Omnia Ibrahim·Amit Meghanani·Nada Almarwani·Hawau Olamide Toyin·Sadeen Alharbi·Modar Alfadly·Lamya Alkanhal·Ibrahim Selim·Shehab Elbatal·Salima Mdhaffar·Thomas Hain·Yasser Hifny·Mostafa Shahin·Ahmed Ali

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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.

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Yassine El Kheir, Omnia Ibrahim, Amit Meghanani, Nada Almarwani, Hawau Olamide Toyin, Sadeen Alharbi, Modar Alfadly, Lamya Alkanhal, Ibrahim Selim, Shehab Elbatal, Salima Mdhaffar, Thomas Hain, Yasser Hifny, Mostafa Shahin, Ahmed Ali. 2025-06-12. Towards a Unified Benchmark for Arabic Pronunciation Assessment: Quranic Recitation as Case Study. https://arxiv.org/abs/2506.07722

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