用语言特定统计图检测跨母语者的发音错误，提升诊断精度。
Domain-Aware Mispronunciation Detection and Diagnosis Using Language-Specific Statistical Graphs
- 构建音素混淆的有向统计图，建模发音错误模式。
- 在L2-ARCTIC数据集上达到59.52%的F1分数，优于基线。
- 适合多语言语音学习系统和发音纠错研究者使用。
近年来，发音错误检测与诊断（MDD）在计算机辅助语言学习和语音技术中变得日益重要。本文提出一种构建统计图的方法，使模型能够以有向图形式学习音素混淆模式。同时，引入一种语言特定策略，捕捉不同母语（L1）背景下的系统性发音差异。在L2-ARCTIC基准上的大量实验验证了该方法的有效性，其F1得分达到59.52%，优于多个竞争性基线。
原文摘要 · Abstract (English)
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.
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