Year: 2025
Type: conference-paper
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 Wav2vec2.0features and trained with CTC loss for phonemelevel 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.
Authors: Mohamed Nadhir Daoud, Mohamed Anouar Ben Messaoud
Institution: Tunis El Manar University
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W4416037778