@inproceedings{nadhir-daoud-anouar-ben-messaoud-2025-phoneme,
title = "Phoneme-level mispronunciation detection in {Q}uranic recitation using {S}hallow{T}ransformer",
author = "Daoud, Mohamed Nadhir and
Ben Messaoud, Mohamed Anouar",
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://aclanthology.org/2025.arabicnlp-sharedtasks.63/",
doi = "10.18653/v1/2025.arabicnlp-sharedtasks.63",
pages = "457--463",
ISBN = "979-8-89176-356-2",
abstract = "Preserving the integrity of Qur{'}anic recitation requires accurate pronunciation, as even subtle mispronunciations can alter meaning. Automatic assessment of Qur{'}anic recitation at the phoneme level is therefore a critical and challenging task. We present ShallowTransformer, a lightweight and computationally efficient transformer model leveraging Wav2vec2.0 features and trained with CTC loss for phoneme-level mispronunciation detection. Evaluated on the Iqra{'}Eval benchmark (QuranMB.v2), our model outperforms published BiLSTM baselines on QuranMB.v1 while achieving competitive performance relative to the official Iqra{'}Eval challenge baselines, which are not yet fully documented. Such improvements are particularly important in assisted Qur{'}an learning, as accurate phonetic feedback supports correct recitation and preserves textual integrity. These results highlight the effectiveness of transformer architectures in capturing subtle pronunciation errors while remaining deployable for practical applications."
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<abstract>Preserving the integrity of Qur’anic recitation requires accurate pronunciation, as even subtle mispronunciations can alter meaning. Automatic assessment of Qur’anic recitation at the phoneme level is therefore a critical and challenging task. We present ShallowTransformer, a lightweight and computationally efficient transformer model leveraging Wav2vec2.0 features and trained with CTC loss for phoneme-level mispronunciation detection. Evaluated on the Iqra’Eval benchmark (QuranMB.v2), our model outperforms published BiLSTM baselines on QuranMB.v1 while achieving competitive performance relative to the official Iqra’Eval challenge baselines, which are not yet fully documented. Such improvements are particularly important in assisted Qur’an learning, as accurate phonetic feedback supports correct recitation and preserves textual integrity. These results highlight the effectiveness of transformer architectures in capturing subtle pronunciation errors while remaining deployable for practical applications.</abstract>
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%0 Conference Proceedings
%T Phoneme-level mispronunciation detection in Quranic recitation using ShallowTransformer
%A Daoud, Mohamed Nadhir
%A Ben Messaoud, Mohamed Anouar
%Y Darwish, Kareem
%Y Ali, Ahmed
%Y Abu Farha, Ibrahim
%Y Touileb, Samia
%Y Zitouni, Imed
%Y Abdelali, Ahmed
%Y Al-Ghamdi, Sharefah
%Y Alkhereyf, Sakhar
%Y Zaghouani, Wajdi
%Y Khalifa, Salam
%Y AlKhamissi, Badr
%Y Almatham, Rawan
%Y Hamed, Injy
%Y Alyafeai, Zaid
%Y Alowisheq, Areeb
%Y Inoue, Go
%Y Mrini, Khalil
%Y Alshammari, Waad
%S Proceedings of The Third Arabic Natural Language Processing Conference: Shared Tasks
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou, China
%@ 979-8-89176-356-2
%F nadhir-daoud-anouar-ben-messaoud-2025-phoneme
%X Preserving the integrity of Qur’anic recitation requires accurate pronunciation, as even subtle mispronunciations can alter meaning. Automatic assessment of Qur’anic recitation at the phoneme level is therefore a critical and challenging task. We present ShallowTransformer, a lightweight and computationally efficient transformer model leveraging Wav2vec2.0 features and trained with CTC loss for phoneme-level mispronunciation detection. Evaluated on the Iqra’Eval benchmark (QuranMB.v2), our model outperforms published BiLSTM baselines on QuranMB.v1 while achieving competitive performance relative to the official Iqra’Eval challenge baselines, which are not yet fully documented. Such improvements are particularly important in assisted Qur’an learning, as accurate phonetic feedback supports correct recitation and preserves textual integrity. These results highlight the effectiveness of transformer architectures in capturing subtle pronunciation errors while remaining deployable for practical applications.
%R 10.18653/v1/2025.arabicnlp-sharedtasks.63
%U https://aclanthology.org/2025.arabicnlp-sharedtasks.63/
%U https://doi.org/10.18653/v1/2025.arabicnlp-sharedtasks.63
%P 457-463
Markdown (Informal)
[Phoneme-level mispronunciation detection in Quranic recitation using ShallowTransformer](https://aclanthology.org/2025.arabicnlp-sharedtasks.63/) (Daoud & Ben Messaoud, ArabicNLP 2025)
ACL