MISPRONUNCIATION DETECTION AND DIAGNOSIS WITHOUT MODEL TRAINING: A RETRIEVAL-BASED APPROACH

Tu Huu Tuong; Huan Vu; Cuong Nguyen Tien; Trang Nguyen Thi Thu

ICASSP 2026oral0 citations

MISPRONUNCIATION DETECTION AND DIAGNOSIS WITHOUT MODEL TRAINING: A RETRIEVAL-BASED APPROACH

Tu Huu Tuong, Huan Vu, Cuong Nguyen Tien, Trang Nguyen Thi Thu

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.

BibTeX

@inproceedings{icassp2026_mispronunciation,
  title = {MISPRONUNCIATION DETECTION AND DIAGNOSIS WITHOUT MODEL TRAINING: A RETRIEVAL-BASED APPROACH},
  author = {Tu Huu Tuong and Huan Vu and Cuong Nguyen Tien and Trang Nguyen Thi Thu},
  booktitle = {ICASSP 2026},
  year = {2026}
}