Casablanca: Data and Models for Multidialectal Arabic Speech Recognition
Abstract
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.
Keywords
Cite
@article{arxiv.2410.04527,
title = {Casablanca: Data and Models for Multidialectal Arabic Speech Recognition},
author = {Bashar Talafha and Karima Kadaoui and Samar Mohamed Magdy and Mariem Habiboullah and Chafei Mohamed Chafei and Ahmed Oumar El-Shangiti and Hiba Zayed and Mohamedou cheikh tourad and Rahaf Alhamouri and Rwaa Assi and Aisha Alraeesi and Hour Mohamed and Fakhraddin Alwajih and Abdelrahman Mohamed and Abdellah El Mekki and El Moatez Billah Nagoudi and Benelhadj Djelloul Mama Saadia and Hamzah A. Alsayadi and Walid Al-Dhabyani and Sara Shatnawi and Yasir Ech-Chammakhy and Amal Makouar and Yousra Berrachedi and Mustafa Jarrar and Shady Shehata and Ismail Berrada and Muhammad Abdul-Mageed},
journal= {arXiv preprint arXiv:2410.04527},
year = {2024}
}