用音位特征提升中文发音错误诊断准确率
Using Phonological-Level Wav2Vec2 for Mandarin Automatic Mispronunciation Detection and Diagnosis
- 在统一的Wav2Vec2 CTC架构中同时建模音段与声调特征
- 误接受率降低10.1%，诊断错误率下降23.6%
- 为二语学习者提供更细致可解释的发音反馈
自动发音错误检测与诊断（MDD）在第二语言中文发音学习中至关重要。尽管端到端（E2E）方法显著提升了音素级检测精度，但诊断反馈仍受限，因音段与声调错误未被明确区分。本文提出一种基于音位特征的MDD框架，在统一的Wav2Vec2 CTC架构中同时建模音段与声调属性。实验结果表明，相比仅基于音素的基线系统，该方法将误接受率（FAR）降低10.1%，诊断错误率（DER）降低23.6%。通过将音素分解为低层级音位成分，该方法使对二语学习者的诊断反馈更加详细且可解释。
原文摘要 · Abstract (English)
Automatic mispronunciation detection and diagnosis (MDD) plays a crucial role in L2 Mandarin pronunciation learning. While end-to-end (E2E) based MDD methods have substantially improved phoneme-level detection accuracy, diagnostic feedback remains limited, as segmental and tonal errors are not explicitly separated. In this paper, we propose a phonological feature-based MDD framework that models both segmental and tonal attributes within a unified Wav2Vec2 CTC architecture. Experimental results show that the proposed method reduces the False Acceptance Rate (FAR) by 10.1% and the Diagnostic Error Rate (DER) by 23.6% compared with the phoneme-only baseline system. By decomposing phonemes into low-level phonological components, the proposed approach enables more detailed and interpretable diagnostic feedback for L2 learners.
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