Efficiently Adapting Spoken Language Models for the Singaporean Context

2026-07-11 · Source: Artificial Intelligence · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Natural Language Processing, Data Science & Analytics · Depth: Expert, quick

Summary

Researchers adapted an open-source Spoken Language Model (SLM) for the Singaporean context, addressing challenges in sensitive, multilingual, spoken-query domains without access to original training data. The resulting HT-Moonstone (5B) model was developed using LoRA fine-tuning, a surrogate text-QA dataset to prevent catastrophic forgetting, and a multi-task objective that adapts the CoBa reweighting scheme for speech. This adaptation process involved five speech tasks across Singapore's four official languages. Additionally, a new HTD-multilingual-QA dataset, comprising 504,853 samples in both text and spoken forms, was created. HT-Moonstone (5B) matches or outperforms SLMs up to 7x its size on most tasks, achieves superior accent and gender recognition, and retains over 98% of its original speech QA ability.

Key takeaway

For Machine Learning Engineers adapting Spoken Language Models to new, multilingual domains with limited data, you should consider the HT-Moonstone approach. This method demonstrates that combining LoRA fine-tuning with a surrogate text-QA dataset and a multi-task objective can yield high performance, even outperforming larger models. You can achieve robust domain adaptation while preserving original capabilities, making it viable for sensitive, resource-constrained environments like Singapore's diverse linguistic landscape.

Key insights

Efficient SLM adaptation for multilingual, sensitive domains is achievable via targeted fine-tuning and synthetic data.

Principles

• LoRA fine-tuning is effective for SLM domain adaptation.
• Surrogate datasets mitigate catastrophic forgetting in SLM adaptation.
• Multi-task objectives enhance multilingual speech adaptation.

Method

Combine LoRA fine-tuning with a surrogate text-QA dataset and a multi-task objective adapting CoBa reweighting to speech for domain-specific SLM adaptation.

In practice

• Use LoRA for efficient SLM adaptation on limited hardware.
• Generate synthetic QA data to preserve base model capabilities.
• Apply CoBa reweighting for multi-task speech objectives.

Topics

• Spoken Language Models
• LoRA Fine-tuning
• Multilingual NLP
• Domain Adaptation
• Catastrophic Forgetting
• HTD-multilingual-QA

Best for: Research Scientist, AI Engineer, AI Scientist, Machine Learning Engineer, NLP Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Artificial Intelligence.