Arabic automatic speech recognition (ASR) faces unique challenges due to diglossia, extensive regional dialect variation, and limited systematically balanced speech resources. Existing speech datasets often focus on single dialects or large-scale broadcast/web data, leading to trade-offs between linguistic diversity and annotation quality. We present BULBUL, a multi-dialect Arabic ASR dataset collected from 275 speakers across 11 Arab countries. BULBUL includes structured dialect and sub-dialect coverage, as well as recordings of classical Arabic and modern standard Arabic spoken by participants in their native dialectal accents to support accent-aware modeling. The quality of the recordings was ensured through a two-level human verification process. We further benchmark a range of recent ASR systems, establishing strong baselines for modern dialectal and accented Arabic ASR.

Arabic speech recognition systems face distinct challenges due to the language’s complex morphology and dialectal variations. Self-supervised models (SSL) like XLS-R have shown promising results, but their size with over than 300 million of parameters, makes fine-tuning computationally expensive. In this work, we present the first comparative study of parameter-efficient fine-tuning (PEFT), specifically LoRA and DoRA, applied to XLS-R for Arabic ASR. We evaluate on the newly released Common Voice Arabic V24.0 dataset, establishing new benchmarks. Our full fine-tuning achieves state-of-the-art results among XLS-R-based models with 23.03% Word Error Rate (WER). In our experiments, LoRA achieved a 36.10% word error rate (WER) while training just 2% of the model’s parameters. DoRA reached 45.20% WER in initial experiments. We analyze the trade-offs between accuracy and efficiency, offering practical guidance for developing Arabic ASR systems when computational resources are limited. The models and code are publicly available.