Nuha-Speech: Building General-Purpose Arabic Speech-LLMs

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.

Computation and Language (cs.CL)

arXiv:2609.11892 [cs.CL]

(or arXiv:2609.11892v1 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2609.11892 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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