ToneCL: Contrastive Learning for Few-Shot Syllable-Level Tone Classification
ToneCL reaches 91.6% few-shot Mandarin tone accuracy using contrastive pretraining on unlabeled speech.
ToneCL is a lightweight contrastive-learning method for few-shot syllable-level tone classification in low-resource tone languages. It is pretrained on unlabeled speech with augmentations that preserve tonal identity, then fine-tuned on tens of examples per class using Mandarin and Vietnamese. On six-speaker Mandarin it reaches 91.6% at 10 shots, and Vietnamese pretraining followed by Mandarin fine-tuning reaches 91.0%. Ablations identify frequency-band rejection as the most important augmentation.
- ToneCL targets few-shot syllable-level tone classification for low-resource languages.
- Pretraining uses unlabeled speech with tone-preserving augmentations.
- Six-speaker Mandarin reaches 91.6% accuracy at 10 shots.
- Vietnamese-to-Mandarin transfer reaches 91.0% at 10 shots.
From the original article
1 min read at arxiv.orgTone 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.
By Qisheng Liao · arxiv.org
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