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arXiv · 2509.14804

Towards Building Speech Large Language Models for Multitask Understanding in Low-Resource Languages

Mingchen Shao·Bingshen Mu·Chengyou Wang·Hai Li·Ying Yan·Zhonghua Fu·Lei Xie
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Abstract

Speech large language models (SLLMs) built on speech encoders, adapters, and LLMs demonstrate remarkable multitask understanding performance in high-resource languages such as English and Chinese. However, their effectiveness substantially degrades in low-resource languages such as Thai. This limitation arises from three factors: (1) existing commonly used speech encoders, like the Whisper family, underperform in low-resource languages and lack support for broader spoken language understanding tasks; (2) the ASR-based alignment paradigm requires training the entire SLLM, leading to high computational cost; (3) paired speech-text data in low-resource languages is scarce. To overcome these challenges in the low-resource language Thai, we introduce XLSR-Thai, the first self-supervised learning (SSL) speech encoder for Thai. It is obtained by continuously training the standard SSL XLSR model on 36,000 hours of Thai speech data. Furthermore, we propose U-Align, a speech-text alignment method that is more resource-efficient and multitask-effective than typical ASR-based alignment. Finally, we present Thai-SUP, a pipeline for generating Thai spoken language understanding data from high-resource languages, yielding the first Thai spoken language understanding dataset of over 1,000 hours. Multiple experiments demonstrate the effectiveness of our methods in building a Thai multitask-understanding SLLM. We open-source XLSR-Thai and Thai-SUP to facilitate future research.

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Mingchen Shao, Bingshen Mu, Chengyou Wang, Hai Li, Ying Yan, Zhonghua Fu, Lei Xie. 2025-09-18. Towards Building Speech Large Language Models for Multitask Understanding in Low-Resource Languages. https://arxiv.org/abs/2509.14804

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