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arXiv · 2603.21900

Ara-Best-RQ: Multi Dialectal Arabic SSL

Haroun Elleuch·Ryan Whetten·Salima Mdhaffar·Yannick Estève·Fethi Bougares
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Abstract

We present Ara-BEST-RQ, a family of self-supervised learning (SSL) models specifically designed for multi-dialectal Arabic speech processing. Leveraging 5,640 hours of crawled Creative Commons speech and combining it with publicly available datasets, we pre-train conformer-based BEST-RQ models up to 600M parameters. Our models are evaluated on dialect identification (DID) and automatic speech recognition (ASR) tasks, achieving state-of-the-art performance on the former while using fewer parameters than competing models. We demonstrate that family-targeted pre-training on Arabic dialects significantly improves downstream performance compared to multilingual or monolingual models trained on non-Arabic data. All models, code, and pre-processed datasets will be publicly released to support reproducibility and further research in Arabic speech technologies.

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Haroun Elleuch, Ryan Whetten, Salima Mdhaffar, Yannick Estève, Fethi Bougares. 2026-03-23. Ara-Best-RQ: Multi Dialectal Arabic SSL. https://arxiv.org/abs/2603.21900

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