用少量发音标注，实现高精度音素级发音评分。
A study on weakly-supervised training approaches for phoneme-level pronunciation scoring
- 用语句或单词级标签训练模型，间接学习音素级错误信息。
- 两阶段训练使性能接近全音素标注效果，仅需少量音素标注数据。
- 适合语音教学系统，降低人工标注成本。
音素级计算机辅助发音训练系统通常依赖昂贵且稀缺的音素级标注。本文研究是否可通过利用更高层级的发音标签，在无需音素级监督的情况下学习音素级发音错误信息。具体而言，我们考察一种弱监督设置：模型仅使用语句级或词级发音标签进行训练，并分析该监督能否诱导出有用的音素级评分预测。此外，我们还考虑两阶段训练场景：先仅用语句级标签训练模型，再用少量精心挑选的音素级标注语句进行微调。结果表明，通过提出的架构和选择流程，两阶段方法在性能上可与全音素级监督相当，且仅需极少量音素级标注数据。
原文摘要 · Abstract (English)
Phoneme-level computer-assisted pronunciation training systems typically rely on phoneme-level annotations, which are costly and scarce. In this work, we investigate whether phoneme-level mispronunciation information can be learned without phoneme-level supervision by exploiting higher-level pronunciation labels. Specifically, we study a weakly supervised setting in which models are trained using only utterance- or word-level pronunciation labels and analyze whether this supervision induces useful phoneme-level score predictions. We further consider a two-stage training scenario in which a model trained only with utterance-level labels is finetuned using a limited number of carefully-selected phoneme-level labeled utterances. We find that, using our proposed architecture and selection process, the two-stage process leads to comparable results to those obtained with full phoneme-level supervision, requiring only a small fraction of phoneme-level labels.
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