@inproceedings{el-kheir-etal-2025-iqraeval,
    title = "Iqra{'}Eval: A Shared Task on Qur{'}anic Pronunciation Assessment",
    author = "El Kheir, Yassine  and
      Meghanani, Amit  and
      Toyin, Hawau Olamide  and
      Almarwani, Nada  and
      Ibrahim, Omnia  and
      Elshahawy, Yousseif Ahmed  and
      Shahin, Mostafa  and
      Ali, Ahmed",
    editor = "Darwish, Kareem  and
      Ali, Ahmed  and
      Abu Farha, Ibrahim  and
      Touileb, Samia  and
      Zitouni, Imed  and
      Abdelali, Ahmed  and
      Al-Ghamdi, Sharefah  and
      Alkhereyf, Sakhar  and
      Zaghouani, Wajdi  and
      Khalifa, Salam  and
      AlKhamissi, Badr  and
      Almatham, Rawan  and
      Hamed, Injy  and
      Alyafeai, Zaid  and
      Alowisheq, Areeb  and
      Inoue, Go  and
      Mrini, Khalil  and
      Alshammari, Waad",
    booktitle = "Proceedings of The Third Arabic Natural Language Processing Conference: Shared Tasks",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/2025.arabicnlp-sharedtasks.61/",
    pages = "443--452",
    ISBN = "979-8-89176-356-2",
    abstract = "We present the findings of the first shared task on Qur{'}anic pronunciation assessment, which focuses on addressing the unique challenges of evaluating the precise pronunciation of Qur{'}anic recitation. To fill an existing research gap, the Iqra{'}Eval 2025 shared task introduces the first open benchmark for Mispronunciation Detection and Diagnosis (MDD) in Qur{'}anic recitation, using Modern Standard Arabic (MSA) reading of Qur{'}anic texts as its case study. The task provides a comprehensive evaluation framework with increasingly complex subtasks: error localization and detailed error diagnosis. Leveraging the recently developed QuranMB benchmark dataset along with auxiliary training resources, this shared task aims to stimulate research in an area of both linguistic and cultural significance while addressing computational challenges in pronunciation assessment."
}