IQRA 2026: Interspeech Challenge on Automatic Pronunciation Assessment for Modern Standard Arabic (MSA)
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2026
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| author | Kheir, Yassine El Meghanani, Amit Shahin, Mostafa Ibrahim, Omnia Chowdhury, Shammur Absar AlMarwani, Nada Elshahawy, Youssef Ali, Ahmed |
| author_facet | Kheir, Yassine El Meghanani, Amit Shahin, Mostafa Ibrahim, Omnia Chowdhury, Shammur Absar AlMarwani, Nada Elshahawy, Youssef Ali, Ahmed |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2603_29087 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | IQRA 2026: Interspeech Challenge on Automatic Pronunciation Assessment for Modern Standard Arabic (MSA) Kheir, Yassine El Meghanani, Amit Shahin, Mostafa Ibrahim, Omnia Chowdhury, Shammur Absar AlMarwani, Nada Elshahawy, Youssef Ali, Ahmed Sound Audio and Speech Processing 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. |
| title | IQRA 2026: Interspeech Challenge on Automatic Pronunciation Assessment for Modern Standard Arabic (MSA) |
| topic | Sound Audio and Speech Processing |
| url | https://arxiv.org/abs/2603.29087 |