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

Yingzhi Wang, Reem Alhazzani, Muhammad Alqurishi1 min readarXivSep 10, 2026

Key Takeaway for Students

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 tra

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This work addresses a critical gap in multilingual speech-LLMs by focusing on Arabic, a low-resource language in this domain. ML engineers can leverage these techniques for developing and evaluating speech-LLMs for other underrepresented languages, expanding their market reach and impact.

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Career Insight

medium demand

Why it matters

Skills to learn

Dataset construction for speech-LLMs (e.g., SQA corpus generation)Supervised fine-tuning of large language models (e.g., Qwen-Omni variants)Designing comprehensive evaluation frameworks for speech tasksTechniques for handling low-resource language data in MLSpeech processing and feature extraction for LLM inputs

Interview questions

Describe the challenges of building speech-LLMs for low-resource languages like Arabic, and how Nuha-Speech attempts to address them.
Given a pre-trained multilingual LLM, what steps would you take to adapt it for a specific low-resource language's speech tasks, considering both data and model architecture?
Design a system for continuously evaluating the performance of a speech-LLM across multiple languages and tasks, ensuring fairness and robustness.

Relevant for ~18 months · generated by the enrichment pipeline