Hybrid Continual Learning for Low-Resource Australian Aboriginal Language Identification
Language identification is an important step toward integrating endangered Australian Aboriginal languages (AALs) into speech technologies supporting language revitalisation and digital inclusion. However, extreme data scarcity limits model performance. Transfer learning from high-resource languages shows promise but often suffers from catastrophic forgetting when adapting to new languages. Continual learning (CL) can mitigate this issue, though it remains challenging with very limited data. To address this, we propose two hybrid continual learning methods: Replay Augmented Elastic Weight Consolidation and Constraint Guided Knowledge Distillation to adapt pretrained speech models for AAL ...
| Authors | Pravina Mylvaganam, Ting Dang, Eliathamby Ambikairajah et al. |
|---|---|
| Primary category | cs.CL |
| Published | 11 July 2026 |
| Last updated at source | 11 July 2026 |
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