Domain-Aware Mispronunciation Detection and Diagnosis Using Language-Specific Statistical Graphs
For researchers in computer-assisted language learning, this work offers a method to improve MDD accuracy by incorporating language-specific pronunciation patterns.
The paper tackles mispronunciation detection and diagnosis (MDD) by proposing language-specific statistical graphs to model phoneme confusion patterns, achieving an F1-score of 59.52% on the L2-ARCTIC benchmark, outperforming several baselines.
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.