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arXiv · 2511.20107

Mispronunciation Detection and Diagnosis Without Model Training: A Retrieval-Based Approach

Huu Tuong Tu·Ha Viet Khanh·Tran Tien Dat·Vu Huan·Thien Van Luong·Nguyen Tien Cuong·Nguyen Thi Thu Trang
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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.

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Huu Tuong Tu, Ha Viet Khanh, Tran Tien Dat, Vu Huan, Thien Van Luong, Nguyen Tien Cuong, Nguyen Thi Thu Trang. 2025-11-25. Mispronunciation Detection and Diagnosis Without Model Training: A Retrieval-Based Approach. https://doi.org/10.1109/icassp55912.2026.11462649

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