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ToneCL addresses the scarcity of labeled data in tone languages by leveraging contrastive learning on unlabeled speech to achieve high accuracy with only tens of examples per class. The framework demonstrates effective cross-lingual transfer between Vietnamese and Mandarin, highlighting frequency band rejection as a key augmentation strategy for preserving tonal identity. This approach significantly advances few-shot learning capabilities for fine-grained syllable-level classification in under-resourced linguistic environments.
ToneCL: Contrastive Learning for Few-Shot Syllable-Level Tone Classification
arXiv:2609.24903v1 Announce Type: new 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.
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.
Computation and Language (cs.CL)
arXiv:2609.24903 [cs.CL]
(or arXiv:2609.24903v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2609.24903 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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