arXiv:2603.06310v2 Announce Type: replace-cross 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 the impact of data volume, adaptation strategies, and representational drift on speech foundation models for various Pacific languages. Additionally, we analyze a continual learning framework for sequential language acquisition. Empirical results across
The proliferation of AI foundation models highlights the limitations of existing datasets and the need for inclusive AI, particularly for low-resource languages, prompting research into continual adaptation strategies.
This research addresses a critical accessibility and equity gap in AI, enabling broader utility and potentially empowering indigenous communities through technology while also informing more robust AI development practices.
AI models can be adapted more effectively for diverse linguistic and cultural contexts, reducing bias and expanding the reach of advanced AI capabilities beyond dominant languages.
- · Pacific Indigenous communities
- · AI researchers
- · Linguistic diversity initiatives
- · Global AI adopters
- · Monolingual AI services
- · Data scarcity-agnostic AI developers
Improved speech recognition for low-resource Pacific Indigenous languages.
Increased digital inclusion and preservation of indigenous languages through AI tools.
Potential for new localized AI applications and services tailored to indigenous cultures, fostering economic and social development.
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Read at arXiv cs.CL