Dialectal Coverage And Generalization in Arabic Speech Recognition
Djanibekov, Amirbek Toyin, Hawau Olamide Alshalan, Raghad A. Alitr, Abdullah Aldarmaki, Hanan
Djanibekov, Amirbek
Toyin, Hawau Olamide
Alshalan, Raghad A.
Alitr, Abdullah
Aldarmaki, Hanan
Author
Djanibekov, Amirbek
Toyin, Hawau Olamide
Alshalan, Raghad A.
Alitr, Abdullah
Aldarmaki, Hanan
Toyin, Hawau Olamide
Alshalan, Raghad A.
Alitr, Abdullah
Aldarmaki, Hanan
Supervisor
Department
Natural Language Processing
Embargo End Date
Type
Conference proceeding
Date
2025
License
Language
English
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Research Projects
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Abstract
Developing robust automatic speech recognition (ASR) systems for Arabic requires effective strategies to manage its diversity. Existing ASR systems mainly cover the modern standard Arabic (MSA) variety and few high-resource dialects, but fall short in coverage and generalization across the multitude of spoken variants. Code-switching with English and French is also common in different regions of the Arab world, which challenges the performance of monolingual Arabic models. In this work, we introduce a suite of ASR models optimized to effectively recognize multiple variants of spoken Arabic, including MSA, various dialects, and code-switching. We provide open-source pre-trained models that cover data from 17 Arabic-speaking countries, and fine-tuned MSA and dialectal ASR models that include at least 11 variants, as well as multi-lingual ASR models covering embedded languages in code-switched utterances. We evaluate ASR performance across these spoken varieties and demonstrate both coverage and performance gains compared to prior models.
Citation
A. Djanibekov, H. O. Toyin, R. Alshalan, A. Alatir, and H. Aldarmaki, “Dialectal Coverage And Generalization in Arabic Speech Recognition,” vol. 1, pp. 29490–29502, Aug. 2025, doi: 10.18653/V1/2025.ACL-LONG.1427.
Source
Proceedings of the Annual Meeting of the Association for Computational Linguistics
Conference
63rd Annual Meeting of the Association for Computational Linguistics, ACL 2025
Keywords
Arabic Speech Recognition, Dialectal Coverage, Generalization to Unseen Dialects, Multilingual & Code-Switched ASR, Low-Resource Dialects, Pre-training with Dialectal Data, Multi-dialectal Fine-tuning
Subjects
Source
63rd Annual Meeting of the Association for Computational Linguistics, ACL 2025
Publisher
Association for Computational Linguistics