A Fusion-Aware Two-Stage Framework for Mispronunciation Detection and Diagnosis in Low-Resource Modern Standard Arabic

Jing Yang; Shuqing Zhang; Yongyi Deng; Pan Li; Ting Dang; Gongping Huang; Jingdong Chen; Jacob Benesty

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arXiv · 2606.24086

A Fusion-Aware Two-Stage Framework for Mispronunciation Detection and Diagnosis in Low-Resource Modern Standard Arabic

Jing Yang·Shuqing Zhang·Yongyi Deng·Pan Li·Ting Dang·Gongping Huang·Jingdong Chen·Jacob Benesty

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Abstract

Accurate phoneme recognition is pivotal for mispronunciation detection and diagnosis (MDD) in modern standard Arabic (MSA), yet remains constrained by data scarcity and the synthetic-real domain gap. This work proposes a two-stage end-to-end framework. It integrates a pre-trained encoder with causal dilated temporal convolutional networks to preserve fine-grained phonetic variations. A hierarchical two-stage strategy first learns general mappings from native/synthetic corpora, then adapts to scarce real learner data to mitigate domain shift without over-correction. Prediction stability is further enhanced via multi-checkpoint ensemble inference with N-gram rescoring. Evaluated on the QuranMB.v2 test set, our system achieves an F1-score of $0.7201$, a $63.1$\% relative improvement over baseline ($0.4414$). This performance ranks at the top of the IqraEval.2 Challenge, establishing a new state-of-the-art for low-resource MSA in MDD.

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Jing Yang, Shuqing Zhang, Yongyi Deng, Pan Li, Ting Dang, Gongping Huang, Jingdong Chen, Jacob Benesty. 2026-06-23. A Fusion-Aware Two-Stage Framework for Mispronunciation Detection and Diagnosis in Low-Resource Modern Standard Arabic. https://arxiv.org/abs/2606.24086

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