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
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.
Topics
Classified with taxonomy v2 on Sat, 5 Sept 2026.