{"ID":3084762,"CreatedAt":"2026-06-05T06:46:15.197025399Z","UpdatedAt":"2026-06-07T01:29:43.904346Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2606.05569","arxiv_id":"2606.05569","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.","short_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. Furt...","url_abs":"https://arxiv.org/abs/2606.05569","url_pdf":"https://arxiv.org/pdf/2606.05569v1","authors":"[\"Huu Tuong Tu\",\"Hanh Nguyen\",\"Thien Van Luong\",\"Nguyen Tien Cuong\",\"Vu Huan\",\"Nguyen Thi Thu Trang\"]","published":"2026-06-04T01:38:11Z","proceeding":"cs.CL","tasks":"[\"cs.CL\",\"cs.SD\",\"eess.AS\"]","methods":"[]","has_code":false}