Problem
Research questions and friction points this paper is trying to address.
Few-Shot Learning
Syllable-Level Tone Classification
Low-Resource Languages
ToneCL: Contrastive Learning for Few-Shot Syllable-Level Tone Classification
🤖 AI Summary
为了解决低资源音调语言的音节级音调分类问题，提出了一种基于对比学习的轻量级框架ToneCL，并在少量标注数据下实现了高精度。
📝 Abstract
Tone languages constitute over 50-70% of the world's languages, but the vast majority are low-resource, lacking the large transcribed corpora needed for automatic tone classification. Existing datasets are typically collected at the sentence level, whereas field linguists require fine-grained syllable-level annotations. We propose ToneCL, a lightweight contrastive learning framework for few-shot syllable-level tone classification. We simulate low-resource conditions on Mandarin and Vietnamese, limiting labeled data to tens of examples per tone class. ToneCL is pretrained on unlabeled speech with augmentations that preserve tonal identity, then fine-tuned on few-shot examples. Experiments show our method consistently outperforms baselines, achieving 91.6% on six-speaker Mandarin at 10 shots. Cross-lingual transfer is also effective: pretraining on Vietnamese and fine-tuning on Mandarin reaches 91.0\% accuracy at 10 shots. Ablation confirms that frequency band rejection is the most critical augmentation.
Innovation
Methods, ideas, or system contributions that make the work stand out.
contrastive learning
few-shot learning
syllable-level tone classification
cross-lingual transfer
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