@inproceedings{sullivan-etal-2026-arab,
    title = "{A}rab Voices: Mapping Standard and Dialectal {A}rabic Speech Technology",
    author = "Sullivan, Peter  and
      Elmadany, AbdelRahim A.  and
      Inciarte, Alcides Alcoba  and
      Abdul-Mageed, Muhammad",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.findings-acl.575/",
    pages = "11843--11878",
    ISBN = "979-8-89176-395-1",
    abstract = "Dialectal Arabic datasets embody a range of domain, dialect, and quality. To better understand the landscape of these datasets, we perform a computational analysis of the `dialectness' and a set of measures of audio quality. This analysis of the training splits of dialectal Arabic datasets, provides a valuable complement to existing literature surveys of dialectal Arabic.To further address inconsistencies between datasets, we also introduce Arab Voices, a standardized framework for supporting Automatic Speech Recognition in dialectal Arabic. This framework provide access to 31 datasets covering 14 dialects, to better address the limited data availability encountered in dialectal Arabic speech processing. Our benchmark further provides a current evaluation of SOTA tools as well as modern multimodal LLMs at dialectal Arabic ASR."
}