mlsanityData to 14 Oct 2026

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

arXiv 2606.05569 ↗First posted 4 Jun 2026cs.CL cs.SD eess.AScited by 0

Not on the map. Why?

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.

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

11 of 34 references.

References · 11

  1. 2017Attention Is All You Needcited by 60,361
  2. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,361
  3. 2016Layer Normalizationcited by 5,669
  4. 2020wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representationscited by 3,905
  5. 2021A Full Text-Dependent End to End Mispronunciation Detection and Diagnosis with Easy Data Augmentation Techniquescited by 19
  6. 2020An End-to-End Mispronunciation Detection System for L2 English Speech Leveraging Novel Anti-Phone Modelingcited by 18
  7. 2021An Approach to Mispronunciation Detection and Diagnosis with Acoustic, Phonetic and Linguistic (APL) Embeddingscited by 8
  8. 2023Multi-View Multi-Task Representation Learning for Mispronunciation Detectioncited by 6
  9. 2021Maximum F1-score training for end-to-end mispronunciation detection and diagnosis of L2 English speechcited by 4
  10. 2022Text-Aware End-to-end Mispronunciation Detection and Diagnosiscited by 3
  11. 2023L1-aware Multilingual Mispronunciation Detection Frameworkcited by 3

Built on by

Not cited yet.