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

TL;DR

This paper reports on the second IQRA Interspeech Challenge for Arabic pronunciation assessment, highlighting new datasets, diverse modeling approaches, and significant performance improvements over the previous edition.

Contribution

The challenge introduced a new authentic mispronounced speech dataset and showcased advanced models leading to improved detection accuracy in Arabic MDD.

Findings

F1-score increased by 0.28 over the previous edition

Diverse approaches like CTC-based models and large audio-language models were employed

Authentic mispronunciation data contributed to performance gains

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.