Mispronunciation Detection and Diagnosis Without Model Training: A Retrieval-Based Approach
Not on the map. Why?
Abstract (arXiv)
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.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 11 of its 21 references that are in the map. First posted on arXiv 25 Nov 2025.
Builds on
11 of 21 references.
References · 11
- 2017Attention Is All You Needcited by 59,932
- 2020wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representationscited by 3,853
- 2021HuBERT: Self-Supervised Speech Representation Learning by Masked Prediction of Hidden Unitscited by 2,301
- 2023Retrieval-Augmented Generation for Large Language Models: A Surveycited by 2,185
- 2022data2vec: A General Framework for Self-supervised Learning in Speech, Vision and Languagecited by 718
- 2023Voice Conversion With Just Nearest Neighborscited by 67
- 2021A Full Text-Dependent End to End Mispronunciation Detection and Diagnosis with Easy Data Augmentation Techniquescited by 19
- 2021An Approach to Mispronunciation Detection and Diagnosis with Acoustic, Phonetic and Linguistic (APL) Embeddingscited by 8
- 2023Multi-View Multi-Task Representation Learning for Mispronunciation Detectioncited by 6
- 2022Text-Aware End-to-end Mispronunciation Detection and Diagnosiscited by 3
- 2023L1-aware Multilingual Mispronunciation Detection Frameworkcited by 3
Built on by
Not cited yet.