Year: 2026
Type: preprint
Abstract: Arabic automatic speech recognition (ASR) faces unique challenges due to diglossia, extensive regional dialect variation, and limited speech resources. Existing speech datasets often focus on single dialects or large-scale broadcast/web data, leading to trade-offs between linguistic diversity and annotation quality. We present BULBUL, a multi-dialect Arabic ASR dataset collected from 275 speakers in 11 Arab countries. BULBUL includes structured dialect and sub-dialect coverage, as well as recordings of classical Arabic and modern standard Arabic spoken by participants in their native dialectal accents to support accent-aware modeling. The quality of the recordings was ensured through a two-level human verification process. We further benchmark a range of recent ASR systems, establishing strong baselines for modern dialectal and accented Arabic ASR.
Source: arXiv (Cornell University)
Authors: Ashraf, Ahmed, Alansari, Aisha, Abbas, Fadel Al, Almarwani, Nada, Aloufi, Samah, Ezzini, Saad, Al-Shaibani, Maged S., Dalaq, Doaa, Elmadany, AbdelRahim A., Abdul-Mageed, Muhammad, Trigui, Mohamed Mehdi, Refai, Dania, Refai, Layan, Akrout, Mohamed, Jarrar, Mustafa, Al-Khatib, Wasfi G., Dalaq, Alaa, El-Nakla, Darin, Abdaljalil, Samir, Al-Fakih, Abdulrahman, Zeghib, Nour El Imane, Redah, Moussa, Chafik, Salmane, El-Attar, Mohamed, Grati, Rima, Kohail, Sarah, Alkhorasani, Malak, Safwan, Khadijah Al, Mudhaffar, Ismail M., Altam, Ali, Al-Shaikh, Ahmed, Saeed, Adnan, Luqman, Hamzah
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7204192110
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