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    <abstract>As Speech Large Language Models (speech-LLMs) become increasingly multilingual, Arabic remains significantly underrepresented, highlighting the need for dedicated infrastructure to train and evaluate Arabic speech-LLMs. To address this gap, we introduce Nuha-Speech, a comprehensive initiative to develop general-purpose Arabic speech-LLMs spanning dataset construction, model training, and systematic evaluation. Specifically, we constructed a large-scale Arabic Speech Question-Answering (SQA) corpus comprising over 1.5 million training samples to allow instruction tuning over a broad range of core speech tasks. Then, the corpus was used for supervised fine-tuning based on Qwen-Omni model variants at different scales. Finally, we designed an evaluation framework featuring diverse tasks and tailored metrics. Through this work, we aim to establish foundational infrastructures for Arabic Speech-LLMs under constraints imposed by limited Arabic speech resources.</abstract>
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%0 Conference Proceedings
%T Nuha-Speech: Building General-Purpose Arabic Speech-LLMs
%A Wang, Yingzhi
%A Alhazzani, Reem
%A AL-Qurishi, Muhammad
%Y T.Y.S.S., Santosh
%Y Ghosh, Sreyan
%Y Duraiswami, Ramani
%Y Watanabe, Shinji
%Y Arora, Siddhant
%Y Kumar, Sonal
%Y Adi, Yossi
%Y Manocha, Dinesh
%Y Garcia Perera, Leibny Paola
%S The Proceedings of Speech and Audio Language Models Workshop (2nd Edition)
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-496-5
%F wang-etal-2026-nuha
%X As Speech Large Language Models (speech-LLMs) become increasingly multilingual, Arabic remains significantly underrepresented, highlighting the need for dedicated infrastructure to train and evaluate Arabic speech-LLMs. To address this gap, we introduce Nuha-Speech, a comprehensive initiative to develop general-purpose Arabic speech-LLMs spanning dataset construction, model training, and systematic evaluation. Specifically, we constructed a large-scale Arabic Speech Question-Answering (SQA) corpus comprising over 1.5 million training samples to allow instruction tuning over a broad range of core speech tasks. Then, the corpus was used for supervised fine-tuning based on Qwen-Omni model variants at different scales. Finally, we designed an evaluation framework featuring diverse tasks and tailored metrics. Through this work, we aim to establish foundational infrastructures for Arabic Speech-LLMs under constraints imposed by limited Arabic speech resources.
%U https://aclanthology.org/2026.salma-1.2/
%P 11-17

Markdown (Informal)

[Nuha-Speech: Building General-Purpose Arabic Speech-LLMs](https://aclanthology.org/2026.salma-1.2/) (Wang et al., SALMA 2026)

• Nuha-Speech: Building General-Purpose Arabic Speech-LLMs (Wang et al., SALMA 2026)

ACL

• Yingzhi Wang, Reem Alhazzani, and Muhammad AL-Qurishi. 2026. Nuha-Speech: Building General-Purpose Arabic Speech-LLMs. In The Proceedings of Speech and Audio Language Models Workshop (2nd Edition), pages 11–17, Budapest, Hungary. Association for Computational Linguistics.