Casablanca: Data and Models for Multidialectal Arabic Speech Recognition
<|reference_start|>Casablanca: Data and Models for Multidialectal Arabic Speech Recognition: In spite of the recent progress in speech processing, the majority of world languages and dialects remain uncovered. This situation only furthers an already wide technological divide, thereby hindering technological and socioeconomic inclusion. This challenge is largely due to the absence of datasets that can empower diverse speech systems. In this paper, we seek to mitigate this obstacle for a number of Arabic dialects by presenting Casablanca, a large-scale community-driven effort to collect and transcribe a multi-dialectal Arabic dataset. The dataset covers eight dialects: Algerian, Egyptian, Emirati, Jordanian, Mauritanian, Moroccan, Palestinian, and Yemeni, and includes annotations for transcription, gender, dialect, and code-switching. We also develop a number of strong baselines exploiting Casablanca. The project page for Casablanca is accessible at: www.dlnlp.ai/speech/casablanca.<|reference_end|>
@article{talafha2024casablanca:,
    title={Casablanca: Data and Models for Multidialectal Arabic Speech Recognition},
    author={Bashar Talafha, Karima Kadaoui, Samar Mohamed Magdy, Mariem
  Habiboullah, Chafei Mohamed Chafei, Ahmed Oumar El-Shangiti, Hiba Zayed,
  Mohamedou cheikh tourad, Rahaf Alhamouri, Rwaa Assi, Aisha Alraeesi, Hour
  Mohamed, Fakhraddin Alwajih, Abdelrahman Mohamed, Abdellah El Mekki, El
  Moatez Billah Nagoudi, Benelhadj Djelloul Mama Saadia, Hamzah A. Alsayadi,
  Walid Al-Dhabyani, Sara Shatnawi, Yasir Ech-Chammakhy, Amal Makouar, Yousra
  Berrachedi, Mustafa Jarrar, Shady Shehata, Ismail Berrada, Muhammad
  Abdul-Mageed},
    journal={arXiv preprint arXiv:2410.04527},
    year={2024},
    archivePrefix={arXiv},
    eprint={2410.04527},
    primaryClass={cs.CL}
}