Efficiently Adapting Spoken Language Models for the Singaporean Context

Researchers adapted an open-source spoken language model (SLM) to the Singaporean Home Team context across five speech tasks in Singapore's four official languages. Using LoRA fine-tuning, a surrogate text-QA dataset, and a multi-task objective, they created HT-Moonstone (5B), which matches or outperforms SLMs up to 7x its size on most tasks and achieves the best accent and gender recognition among evaluated models.

arXiv:2607.10092v1 Announce Type: new Abstract: Spoken language models SLMs unify speech perception and reasoning, but adapting them to sensitive domains is underexplored, especially when the original training data is inaccessible and the use case demands multilingual, spoken-query interaction. We adapt an open-source SLM to the Singaporean Home Team context across five speech tasks in Singapore's four official languages, combining LoRA fine-tuning, a surrogate text-QA dataset that guards against catastrophic forgetting, and a multi-task objective that adapts the CoBa reweighting scheme to speech. We also build HTD-multilingual-QA, a 504,853 sample multilingual QA dataset in text and spoken form. The resulting HT-Moonstone 5B matches or outperforms SLMs up to 7x its size on most tasks, attains the best accent and gender recognition among all models evaluated, and loses under 2\% of its original speech QA ability.