# Overcoming Data Scarcity in Multi-Dialectal Arabic ASR via Whisper Fine-Tuning

arXiv: https://arxiv.org/abs/2506.02627 · HTML: https://mlsanity.com/paper/2506.02627/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 16 references that are in the map.
- First posted on arXiv 3 Jun 2025.

## Builds on (most cited first, 8 of 8)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Robust Speech Recognition via Large-Scale Weak Supervision](https://mlsanity.com/paper/2212.04356/) (2022)
- [HuggingFace's Transformers: State-of-the-art Natural Language Processing](https://mlsanity.com/paper/1910.03771/) (2019)
- [Common Voice: A Massively-Multilingual Speech Corpus](https://mlsanity.com/paper/1912.06670/) (2019)
- [Google USM: Scaling Automatic Speech Recognition Beyond 100 Languages](https://mlsanity.com/paper/2303.01037/) (2023)
- [Adapting Whisper for Regional Dialects: Enhancing Public Services for Vulnerable Populations in the United Kingdom](https://mlsanity.com/paper/2501.08502/) (2025)
- [Whisper Finetuning on Nepali Language](https://mlsanity.com/paper/2411.12587/) (2024)

## Built on by (most cited first, 7 of 7)

- [Doing More with Less: Data Augmentation for Sudanese Dialect Automatic Speech Recognition](https://mlsanity.com/paper/2601.06802/) (2026)
- [Dziri Voicebot: An End-to-End Low-Resource Speech-to-Speech Conversational System for Algerian Dialect](https://mlsanity.com/paper/2606.26003/) (2026)
- [myMediWhisper: Construction of Burmese Medical Speech Corpus and Whisper Fine-Tuning for Clinical Dialogue ASR](https://mlsanity.com/paper/2608.11036/) (2026)
- [BuzzASR: A Swarm of 100+ Monolingual Speech Recognition Models](https://mlsanity.com/paper/2609.09554/) (2026)
- [NADI 2026: The Second Multidialectal Arabic Speech Processing Shared Task](https://mlsanity.com/paper/2609.27086/) (2026)
- [Spooftral: Can Voxtral Audio-Language Model Detect Speech Spoofing?](https://mlsanity.com/paper/2609.28713/) (2026)
- [CARDAMOM: A Micro-Dialectal Arabic Speech Dataset for ASR](https://mlsanity.com/paper/2609.34481/) (2026)

## Abstract (arXiv)

Although commercial Arabic automatic speech recognition (ASR) systems support Modern Standard Arabic (MSA), they struggle with dialectal speech. We investigate the effect of fine-tuning OpenAI's Whisper on five major Arabic dialects (Gulf, Levantine, Iraqi, Egyptian, Maghrebi) using Mozilla Common Voice for MSA and the MASC dataset for dialectal speech. We evaluate MSA training size effects, benefits of pre-training on MSA data, and dialect-specific versus dialect-pooled models. We find that small amounts of MSA fine-tuning data yield substantial improvements for smaller models, matching larger non-fine-tuned models. While MSA pre-training shows minimal benefit, suggesting limited shared features between MSA and dialects, our dialect-pooled models perform comparably to dialect-specific ones. This indicates that pooling dialectal data, when properly balanced, can help address data scarcity in low-resource ASR without significant performance loss.