Continual Adaptation for Pacific Indigenous Speech Recognition

2026-03-06

AI Summary

This study addresses the poor performance of speech foundation models on extremely low-resource Indigenous Pacific languages and the catastrophic forgetting induced by full fine-tuning. The authors propose a multilingual sequential learning framework to systematically evaluate full fine-tuning against Low-Rank Adaptation (LoRA) for continual speech adaptation. Their analysis reveals critical challenges in continual learning for distantly related, low-resource languages, including representational drift and the stability-plasticity trade-off. While LoRA demonstrates initial effectiveness, it still suffers from significant forgetting over multilingual sequential training. This work underscores the urgent need—and substantial difficulty—of developing robust, sustainable adaptation strategies for marginalized languages in continual learning settings.

Technology Category

• Machine Learning: Life-Long and Continual Learning
• Natural Language Processing: Language Grounding & Multi-modal NLP
• Search and Optimization: Learning to Search

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

Speech foundation models struggle with low-resource Pacific Indigenous languages because of severe data scarcity. Furthermore, full fine-tuning risks catastrophic forgetting. To address this gap, we present an empirical study adapting models to real-world Pacific datasets. We investigate how data volume and linguistic features affect adaptation success. Specifically, we evaluate strategies including Full Fine-Tuning and Low-Rank Adaptation (LoRA). Additionally, we analyze a continual learning framework for sequentially acquiring multiple languages. We demonstrate that adapting to these distant languages causes severe internal representational drift. Consequently, these models face a strict plasticity and stability dilemma. While LoRA adapts well initially, it suffers from catastrophic forgetting during sequential learning. Ultimately, this study highlights the urgent need for robust adaptation strategies tailored to underrepresented languages.

Problem

• low-resource languages
• catastrophic forgetting
• continual learning
• speech recognition
• representational drift

Innovation

• Continual Learning
• Low-Resource Speech Recognition
• Catastrophic Forgetting
• Low-Rank Adaptation (LoRA)
• Representational Drift