Efficiently Adapting Spoken Language Models for the Singaporean Context

Jul 14, 2026

Researchers adapted an open-source spoken language model to the Singaporean Home Team domain, covering five speech tasks in the country's four official languages. By combining LoRA fine-tuning, a surrogate text-QA dataset, and a multi-task objective, they developed HT-Moonstone (5B), which matches or outperforms models up to seven times its size on most tasks and achieves the best accent and gender recognition among evaluated models.

Why it matters: This work presents a practical approach for adapting spoken language models to sensitive, multilingual domains without access to original training data, achieving strong results with a relatively small model.

Full story at: arXiv Computation and Language ↗