Efficiently Adapting Spoken Language Models for the Singaporean Context
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Computer Science > Computation and Language
Title:Efficiently Adapting Spoken Language Models for the Singaporean Context
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.
| Comments: | 10 pages, 2 figures |
| Subjects: | Computation and Language (cs.CL); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2607.10092 [cs.CL] |
| (or arXiv:2607.10092v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2607.10092
arXiv-issued DOI via DataCite (pending registration)
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Submission history
From: Jia Sheng Jason Ng [view email][v1] Sat, 11 Jul 2026 03:11:26 UTC (333 KB)
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