arXiv AI By Ahmed Ashraf, Aisha Alansari, Fadel Al Abbas, Nada Almarwani, Samah Aloufi, Saad Ezzini, Maged S. Al-Shaibani, Doaa Dalaq, AbdelRahim A. Elmadany, Muhammad Abdul-Mageed, Mohamed Mehdi Trigui, Dania Refai, Layan Refai, Mohamed Akrout, Mustafa Jarrar, Wasfi G. Al-Khatib, Alaa Dalaq, Darin El-Nakla, Samir Abdaljalil, Abdulrahman Al-Fakih, Nour El Imane Zeghib, Moussa Redah, Salmane Chafik, Mohamed El-Attar, Rima Grati, Sarah Kohail, Malak Alkhorasani, Khadijah Al Safwan, Ismail M. Mudhaffar, Ali Altam, Ahmed Al-Shaikh, Adnan Saeed, Hamzah Luqman

Bulbul: A Dataset for Dialectal Arabic Speech Recognition

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arXiv Computation and Language
Sep 24

NADI 2026: The Second Multidialectal Arabic Speech Processing Shared Task

NADI 2026 is the seventh edition of the Nuanced Arabic Dialect Identification shared task series and the second focused on multidialectal Arabic speech processing. It includes five main tasks—Automatic Speech Recognition, Spoken Dialect Identification, Text-to-Speech, Spoken Language Translation, and Spoken Language Understanding—along with eight subtasks that test realistic scenarios such as low‑bandwidth, mixed dialects, code‑switching, out‑of‑domain, and zero‑shot settings. The event attracted 21 teams from at least 13 countries, with 48 test‑phase submissions and 14 system‑description papers, and the results highlight out‑of‑domain generalization as a major bottleneck while showcasing the strengths of Arabic‑specialized speech models, multimodal dialect identification, and ensemble methods.

By Peter Sullivan, Bashar Talafha, Ahmed Ashraf, Fethi Bougares, Haroun Elleuch, Chiyu Zhang, AbdelRahim Elmadany, Youssef Mohamed, Salima Mdhaffar, Yannick Est\`eve, Mohamed Elhoseiny, Hamzah Luqman, Nizar Habash, Muhammad Abdul-Mageed
arXiv Computation and Language
Sep 11

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

Nuha‑Speech is a new initiative aimed at creating general‑purpose Arabic speech‑large language models (speech‑LLMs). It includes the construction of a large Arabic Speech Question‑Answering corpus with over 1.5 million samples for instruction tuning, supervised fine‑tuning of Qwen‑Omni model variants at various scales, and a systematic evaluation framework with diverse tasks and tailored metrics. The project seeks to establish foundational infrastructure for Arabic speech‑LLMs amid limited Arabic speech resources.

By Yingzhi Wang, Reem Alhazzani, Muhammad Alqurishi