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The IQRA 2026 Interspeech Challenge focused on automatic Mispronunciation Detection and Diagnosis (MDD) for Modern Standard Arabic (MSA), introducing a new dataset of authentic mispronounced speech called Iqra\_Extra\_IS26. Participating systems leveraged CTC-based self-supervised learning, two-stage fine-tuning, and large audio-language models to tackle the MDD task. The challenge saw a significant performance increase of 0.28 in F1-score compared to the previous edition, driven by both improved models and the new dataset.
Arabic mispronunciation detection just got a whole lot better: F1-scores jumped by 0.28 thanks to novel architectures and a new dataset of authentic mispronunciations.
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.
