ToneCL: Contrastive Learning for Few-Shot Syllable-Level Tone Classification

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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