[Submitted on 11 Jul 2026]
Title:Hybrid Continual Learning for Low-Resource Australian Aboriginal Language Identification
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Abstract: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 identification while preserving previously learned knowledge. Experiments on Warlpiri, Dalabon and Dharawal show that the proposed methods outperform fine-tuning and existing CL baselines, improving adaptation to multiple AALs while maintaining performance on previously learnt high-resource languages.
Comments: Accepted by Interspeech 2026
Subjects:
Computation and Language (cs.CL); Audio and Speech Processing (eess.AS)
Cite as: arXiv:2607.11946 [cs.CL]
(or arXiv:2607.11946v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2607.11946
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Pravina Mylvaganam [view email] [v1] Sat, 11 Jul 2026 11:01:56 UTC (248 KB)
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