Domain-Aware Mispronunciation Detection and Diagnosis Using Language-Specific Statistical Graphs
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Computer Science > Computation and Language
Title:Domain-Aware Mispronunciation Detection and Diagnosis Using Language-Specific Statistical Graphs
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.
| Comments: | Accepted at Interspeech 2026 |
| Subjects: | Computation and Language (cs.CL); Sound (cs.SD); Audio and Speech Processing (eess.AS) |
| Cite as: | arXiv:2606.05569 [cs.CL] |
| (or arXiv:2606.05569v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2606.05569
arXiv-issued DOI via DataCite (pending registration)
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