Year: 2025
Type: conference-paper
Abstract: This paper describes AraS2P, our speech-tophonemes system submitted to the Iqra'Eval 2025 Shared Task.We adapted Wav2Vec2-BERT via Two-Stage training strategy.In the first stage, task-adaptive continue pretraining was performed on large-scale Arabic speechphonemes datasets, which were generated by converting the Arabic text using the MSA Phonetiser.In the second stage, the model was fine-tuned on the official shared task data, with additional augmentation from XTTS-v2synthesized recitations featuring varied Ayat segments, speaker embeddings, and textual perturbations to simulate possible human errors.The system ranked first on the official leaderboard, demonstrating that phoneme-aware pretraining combined with targeted augmentation yields strong performance in phoneme-level mispronunciation detection.
Authors: Bassam Mattar, Mohamed Fayed, Ayman Khalafallah
Institutions: Alexandria University, Georgia Institute of Technology
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W4416037785