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arXiv · 2603.29087

IQRA 2026: Interspeech Challenge on Automatic Pronunciation Assessment for Modern Standard Arabic (MSA)

Yassine El Kheir·Amit Meghanani·Mostafa Shahin·Omnia Ibrahim·Shammur Absar Chowdhury·Nada AlMarwani·Youssef Elshahawy·Ahmed Ali
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Abstract

We present the findings of the second edition of the IQRA Interspeech Challenge, a challenge on automatic Mispronunciation Detection and Diagnosis (MDD) for Modern Standard Arabic (MSA). Building on the previous edition, this iteration introduces \textbf{Iqra\_Extra\_IS26}, a new dataset of authentic human mispronounced speech, complementing the existing training and evaluation resources. Submitted systems employed a diverse range of approaches, spanning CTC-based self-supervised learning models, two-stage fine-tuning strategies, and using large audio-language models. Compared to the first edition, we observe a substantial jump of \textbf{0.28 in F1-score}, attributable both to novel architectures and modeling strategies proposed by participants and to the additional authentic mispronunciation data made available. These results demonstrate the growing maturity of Arabic MDD research and establish a stronger foundation for future work in Arabic pronunciation assessment.

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Yassine El Kheir, Amit Meghanani, Mostafa Shahin, Omnia Ibrahim, Shammur Absar Chowdhury, Nada AlMarwani, Youssef Elshahawy, Ahmed Ali. 2026-03-31. IQRA 2026: Interspeech Challenge on Automatic Pronunciation Assessment for Modern Standard Arabic (MSA). https://arxiv.org/abs/2603.29087

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