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.
Abstract (translated)
语音基础模型在处理低资源的太平洋土著语时面临挑战，主要是由于数据稀缺。此外，进行全面微调会带来灾难性遗忘的风险。为了解决这一问题，我们进行了一项实证研究，探讨如何将这些模型适应于现实世界的太平洋语言数据集。我们的研究考察了数据量和语言特征对适应效果的影响，并评估了几种策略的有效性，包括全面微调（Full Fine-Tuning）和低秩自适应（Low-Rank Adaptation, LoRA）。此外，我们还分析了一个连续学习框架，该框架能够顺序地获取多种语言。我们的研究发现，在这些远距离的语言上进行适应会导致严重的内部表示漂移问题。因此，这些模型面临着严格的可塑性和稳定性困境。 虽然LoRA在初始阶段表现良好，但在顺序学习过程中会遭遇灾难性遗忘的问题。最终，这项研究表明，急需为那些代表性不足的土著语言制定稳健的自适应策略。
URL
https://arxiv.org/abs/2603.06310
