用音位知识约束替换，提升无对齐发音评估效率与准确率
Enhancing GOP in CTC-Based Mispronunciation Detection with Phonological Knowledge
- 基于音位聚类和学习者常见错误，限制音素替换范围
- 在两个英语二语数据集上，受限替换比无限制替换效果更好
- 适合需要高效精准发音评估的语音训练系统研发者
计算机辅助发音训练系统使用发音质量度量指标，如发音好坏（GOP）评分。传统GOP依赖强制对齐，易受声学变异影响产生标注与切分错误。尽管无对齐方法可缓解此问题，但计算成本高且随音素序列长度和音素库规模增长而恶化。为此，我们提出一种替换感知的无对齐GOP，通过音位聚类和学习者常见错误限制音素替换。我们在两个二语英语语音数据集上进行评估：包含儿童语音的My Pronunciation Coach（MPC）和涵盖儿童与成人语音的SpeechOcean762。对比了受限（RPS）与无限制（UPS）音素替换设置，结果显示无对齐方法优于基线。讨论结果并展望未来研究方向。
原文摘要 · Abstract (English)
Computer-Assisted Pronunciation Training (CAPT) systems employ automatic measures of pronunciation quality, such as the goodness of pronunciation (GOP) metric. GOP relies on forced alignments, which are prone to labeling and segmentation errors due to acoustic variability. While alignment-free methods address these challenges, they are computationally expensive and scale poorly with phoneme sequence length and inventory size. To enhance efficiency, we introduce a substitution-aware alignment-free GOP that restricts phoneme substitutions based on phoneme clusters and common learner errors. We evaluated our GOP on two L2 English speech datasets, one with child speech, My Pronunciation Coach (MPC), and SpeechOcean762, which includes child and adult speech. We compared RPS (restricted phoneme substitutions) and UPS (unrestricted phoneme substitutions) setups within alignment-free methods, which outperformed the baseline. We discuss our results and outline avenues for future research.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。