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Casablanca: Data and Models for Multidialectal Arabic Speech Recognition

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arxiv 2410.04527 v1 pith:EQHMA4IA submitted 2024-10-06 cs.CL

Bashar Talafha , Karima Kadaoui , Samar Mohamed Magdy , Mariem Habiboullah , Chafei Mohamed Chafei , Ahmed Oumar El-Shangiti , Hiba Zayed , Mohamedou cheikh tourad
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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
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classification cs.CL
keywords casablancaspeecharabicdialectsdatasetnumbertechnologicalabsence
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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.

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Cited by 4 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. The ML-SUPERB 2.0 Challenge: Towards Inclusive ASR Benchmarking for All Language Varieties

    cs.CL 2025-09 conditional novelty 6.0 of 10

    The ML-SUPERB 2.0 Challenge adds accented and dialectal speech evaluation to multilingual ASR, and all five submitted systems beat the baselines.

  2. NADI 2025: The First Multidialectal Arabic Speech Processing Shared Task

    cs.CL 2025-09 conditional novelty 5.0 of 10

    The NADI 2025 shared task introduces a standardized speech benchmark for eight Arabic dialects and reports best results of 79.8% dialect ID accuracy, 35.68 WER for ASR, and 55 WER for diacritic restoration.

  3. Advancing Arabic Speech Recognition Through Large-Scale Weakly Supervised Learning

    cs.AI 2025-04 conditional novelty 5.0 of 10

    A Conformer-based Arabic ASR trained from scratch on 15,000 hours of weak labels outperforms several open and closed-source models on standard Arabic benchmarks.

  4. Open Universal Arabic ASR Leaderboard

    cs.CL 2024-12 conditional novelty 5.0 of 10

    A new Arabic ASR leaderboard ranks 14 open-source models on five multi-dialect datasets and analyzes robustness, speaker bias, and efficiency.

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