# Domain-Aware Mispronunciation Detection and Diagnosis Using Language-Specific Statistical Graphs

arXiv: https://arxiv.org/abs/2606.05569 · HTML: https://mlsanity.com/paper/2606.05569/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 34 references that are in the map.
- First posted on arXiv 4 Jun 2026.

## Builds on (most cited first, 11 of 11)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Layer Normalization](https://mlsanity.com/paper/1607.06450/) (2016)
- [wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations](https://mlsanity.com/paper/2006.11477/) (2020)
- [A Full Text-Dependent End to End Mispronunciation Detection and Diagnosis with Easy Data Augmentation Techniques](https://mlsanity.com/paper/2104.08428/) (2021)
- [An End-to-End Mispronunciation Detection System for L2 English Speech Leveraging Novel Anti-Phone Modeling](https://mlsanity.com/paper/2005.11950/) (2020)
- [An Approach to Mispronunciation Detection and Diagnosis with Acoustic, Phonetic and Linguistic (APL) Embeddings](https://mlsanity.com/paper/2110.07274/) (2021)
- [Multi-View Multi-Task Representation Learning for Mispronunciation Detection](https://mlsanity.com/paper/2306.01845/) (2023)
- [Maximum F1-score training for end-to-end mispronunciation detection and diagnosis of L2 English speech](https://mlsanity.com/paper/2108.13816/) (2021)
- [Text-Aware End-to-end Mispronunciation Detection and Diagnosis](https://mlsanity.com/paper/2206.07289/) (2022)
- [L1-aware Multilingual Mispronunciation Detection Framework](https://mlsanity.com/paper/2309.07719/) (2023)

## Abstract (arXiv)

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.