阿拉伯语发音诊断挑战赛升级，新数据集助力自动纠错能力提升
IQRA 2026: Interspeech Challenge on Automatic Pronunciation Assessment for Modern Standard Arabic (MSA)
- 引入真实人类发音错误数据集，增强模型训练效果
- F1得分提升0.28，验证方法与数据双重改进的有效性
- 适合语音识别、语言教育领域研究者参考
我们呈现了第二届IQRA Interspeech挑战赛的成果，该挑战聚焦于现代标准阿拉伯语（MSA）的自动发音错误检测与诊断（MDD）。在上一届基础上，本届新增了真实人类发音错误数据集Iqra_Extra_IS26，补充了现有训练与评估资源。参赛系统采用多样方法，包括基于CTC的自监督学习模型、两阶段微调策略以及大型音频-语言模型。相比首届，F1得分显著提升0.28，归因于参与者提出的新型架构与建模策略，以及新增的真实发音错误数据。这些结果表明阿拉伯语发音诊断研究日趋成熟，并为未来工作奠定了更坚实基础。
原文摘要 · Abstract (English)
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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