arXiv:2601.12494v3 Announce Type: replace-cross Abstract: Audio large language models (LLMs) enable unified speech understanding and generation, but adapting them to linguistically complex and dialect-rich settings such as Arabic-English remains challenging. We present a controlled study of multi-task instruction tuning for an Arabic-centric audio LLM across generative tasks, including automatic speech recognition (ASR) and speech and text summarization, as well as discriminative tasks, including dialect identification (DID) and speech emotion recognition (SER), in a resource-constrained setti
The continuous advancements in LLM foundational models are enabling specialized adaptations, pushing the boundaries of what is possible in multilingual and low-resource environments within AI. This research reflects an ongoing effort to democratize advanced AI capabilities beyond dominant languages and well-resourced contexts.
This work demonstrates progress in making sophisticated AI accessible to linguistically diverse and resource-constrained regions, which can foster localized innovation and reduce dependence on dominant AI ecosystems. It addresses critical challenges in multilingual AI, particularly for complex languages like Arabic.
The ability to effectively train multi-task audio LLMs for Arabic, even with limited resources, changes the landscape for speech technology development in regions previously underserved. It paves the way for more nuanced and culturally relevant AI applications.
- · Arabic-speaking populations
- · Middle Eastern tech sector
- · Multilingual AI developers
- · Speech technology companies
- · Monolingual AI solutions
- · Companies relying on high-resource language data
Improved speech understanding and generation capabilities for Arabic across various applications.
Increased adoption of AI tools and services in Arabic-speaking countries, driving local digital economies.
Enhanced geopolitical influence for nations that successfully develop and deploy sovereign, culturally specific AI, potentially reducing reliance on foreign tech stacks.
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Read at arXiv cs.AI