{"ID":2836859,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2511.20107","arxiv_id":"2511.20107","title":"Mispronunciation Detection and Diagnosis Without Model Training: A Retrieval-Based Approach","abstract":"Mispronunciation Detection and Diagnosis (MDD) is crucial for language learning and speech therapy. Unlike conventional methods that require scoring models or training phoneme-level models, we propose a novel training-free framework that leverages retrieval techniques with a pretrained Automatic Speech Recognition model. Our method avoids phoneme-specific modeling or additional task-specific training, while still achieving accurate detection and diagnosis of pronunciation errors. Experiments on the L2-ARCTIC dataset show that our method achieves a superior F1 score of 69.60% while avoiding the complexity of model training.","short_abstract":"Mispronunciation Detection and Diagnosis (MDD) is crucial for language learning and speech therapy. Unlike conventional methods that require scoring models or training phoneme-level models, we propose a novel training-free framework that leverages retrieval techniques with a pretrained Automatic Speech Recognition mode...","url_abs":"https://arxiv.org/abs/2511.20107","url_pdf":"https://arxiv.org/pdf/2511.20107v1","authors":"[\"Huu Tuong Tu\",\"Ha Viet Khanh\",\"Tran Tien Dat\",\"Vu Huan\",\"Thien Van Luong\",\"Nguyen Tien Cuong\",\"Nguyen Thi Thu Trang\"]","published":"2025-11-25T09:26:34Z","proceeding":"cs.CL","tasks":"[\"cs.CL\",\"cs.SD\",\"eess.AS\"]","methods":"[]","has_code":false}