Towards a Unified Benchmark for Arabic Pronunciation Assessment: Quranic Recitation as Case Study
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arXiv
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| Main Authors: | , , , , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866918056264990720 |
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| author | Kheir, Yassine El Ibrahim, Omnia Meghanani, Amit Almarwani, Nada Toyin, Hawau Olamide Alharbi, Sadeen Alfadly, Modar Alkanhal, Lamya Selim, Ibrahim Elbatal, Shehab Mdhaffar, Salima Hain, Thomas Hifny, Yasser Shahin, Mostafa Ali, Ahmed |
| author_facet | Kheir, Yassine El Ibrahim, Omnia Meghanani, Amit Almarwani, Nada Toyin, Hawau Olamide Alharbi, Sadeen Alfadly, Modar Alkanhal, Lamya Selim, Ibrahim Elbatal, Shehab Mdhaffar, Salima Hain, Thomas Hifny, Yasser Shahin, Mostafa Ali, Ahmed |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_07722 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Towards a Unified Benchmark for Arabic Pronunciation Assessment: Quranic Recitation as Case Study Kheir, Yassine El Ibrahim, Omnia Meghanani, Amit Almarwani, Nada Toyin, Hawau Olamide Alharbi, Sadeen Alfadly, Modar Alkanhal, Lamya Selim, Ibrahim Elbatal, Shehab Mdhaffar, Salima Hain, Thomas Hifny, Yasser Shahin, Mostafa Ali, Ahmed Sound Audio and Speech Processing 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. |
| title | Towards a Unified Benchmark for Arabic Pronunciation Assessment: Quranic Recitation as Case Study |
| topic | Sound Audio and Speech Processing |
| url | https://arxiv.org/abs/2506.07722 |