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arXiv · 2606.05569

Domain-Aware Mispronunciation Detection and Diagnosis Using Language-Specific Statistical Graphs

Huu Tuong Tu·Hanh Nguyen·Thien Van Luong·Nguyen Tien Cuong·Vu Huan·Nguyen Thi Thu Trang
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Abstract

Mispronunciation Detection and Diagnosis (MDD) has gained increasing importance in computer-assisted language learning and speech technology in recent years. In this paper, we propose a method for constructing statistical graphs that enable models to learn phoneme confusion patterns represented as directed graphs. Furthermore, we introduce a language-specific strategy to capture systematic pronunciation differences across various native language (L1) backgrounds. The effectiveness of our approach is demonstrated through extensive experiments on the L2-ARCTIC benchmark, where it achieves an F1-score of 59.52%, outperforming several competitive baselines.

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Huu Tuong Tu, Hanh Nguyen, Thien Van Luong, Nguyen Tien Cuong, Vu Huan, Nguyen Thi Thu Trang. 2026-06-04. Domain-Aware Mispronunciation Detection and Diagnosis Using Language-Specific Statistical Graphs. https://arxiv.org/abs/2606.05569

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