# N-Shot Benchmarking of Whisper on Diverse Arabic Speech Recognition arXiv: https://arxiv.org/abs/2306.02902 ยท HTML: https://mlsanity.com/paper/2306.02902/ ## Key facts - Cited by 20 papers in the map (data through 14 Oct 2026). - Builds on 17 of its 25 references that are in the map. - First posted on arXiv 5 Jun 2023. ## Builds on (most cited first, 17 of 17) - [Robust Speech Recognition via Large-Scale Weak Supervision](https://mlsanity.com/paper/2212.04356/) (2022) - [wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations](https://mlsanity.com/paper/2006.11477/) (2020) - [HuBERT: Self-Supervised Speech Representation Learning by Masked Prediction of Hidden Units](https://mlsanity.com/paper/2106.07447/) (2021) - [Common Voice: A Massively-Multilingual Speech Corpus](https://mlsanity.com/paper/1912.06670/) (2019) - [XLS-R: Self-supervised Cross-lingual Speech Representation Learning at Scale](https://mlsanity.com/paper/2111.09296/) (2021) - [Pre-trained Models for Natural Language Processing: A Survey](https://mlsanity.com/paper/2003.08271/) (2020) - [FLEURS: Few-shot Learning Evaluation of Universal Representations of Speech](https://mlsanity.com/paper/2205.12446/) (2022) - [W2v-BERT: Combining Contrastive Learning and Masked Language Modeling for Self-Supervised Speech Pre-Training](https://mlsanity.com/paper/2108.06209/) (2021) - [VoxLingua107: a Dataset for Spoken Language Recognition](https://mlsanity.com/paper/2011.12998/) (2020) - [Self-Supervised Learning for Videos: A Survey](https://mlsanity.com/paper/2207.00419/) (2022) - [Audio Self-supervised Learning: A Survey](https://mlsanity.com/paper/2203.01205/) (2022) - [Speech Recognition Challenge in the Wild: Arabic MGB-3](https://mlsanity.com/paper/1709.07276/) (2017) - [Towards One Model to Rule All: Multilingual Strategy for Dialectal Code-Switching Arabic ASR](https://mlsanity.com/paper/2105.14779/) (2021) - [Automatic Pronunciation Assessment using Self-Supervised Speech Representation Learning](https://mlsanity.com/paper/2204.03863/) (2022) - [Analyzing Phonetic and Graphemic Representations in End-to-End Automatic Speech Recognition](https://mlsanity.com/paper/1907.04224/) (2019) - [Arabic Speech Recognition by End-to-End, Modular Systems and Human](https://mlsanity.com/paper/2101.08454/) (2021) - [There is more than one kind of robustness: Fooling Whisper with adversarial examples](https://mlsanity.com/paper/2210.17316/) (2022) ## Built on by (most cited first, 20 of 20) - [Can Whisper perform speech-based in-context learning?](https://mlsanity.com/paper/2309.07081/) (2023) - [Kid-Whisper: Towards Bridging the Performance Gap in Automatic Speech Recognition for Children VS. Adults](https://mlsanity.com/paper/2309.07927/) (2023) - [Casablanca: Data and Models for Multidialectal Arabic Speech Recognition](https://mlsanity.com/paper/2410.04527/) (2024) - [Lost in Transcription, Found in Distribution Shift: Demystifying Hallucination in Speech Foundation Models](https://mlsanity.com/paper/2502.12414/) (2025) - [Adapting Whisper for Parameter-efficient Code-Switching Speech Recognition via Soft Prompt Tuning](https://mlsanity.com/paper/2506.21576/) (2025) - [To Distill or Not to Distill? On the Robustness of Robust Knowledge Distillation](https://mlsanity.com/paper/2406.04512/) (2024) - [uDistil-Whisper: Label-Free Data Filtering for Knowledge Distillation in Low-Data Regimes](https://mlsanity.com/paper/2407.01257/) (2024) - [Open Universal Arabic ASR Leaderboard](https://mlsanity.com/paper/2412.13788/) (2024) - [VoxArabica: A Robust Dialect-Aware Arabic Speech Recognition System](https://mlsanity.com/paper/2310.11069/) (2023) - [ADI-20: Arabic Dialect Identification dataset and models](https://mlsanity.com/paper/2511.10070/) (2025) - [TEDxTN: A Three-way Speech Translation Corpus for Code-Switched Tunisian Arabic - English](https://mlsanity.com/paper/2511.10780/) (2025) - [What Do Prosody and Text Convey? Characterizing How Meaningful Information is Distributed Across Multiple Channels](https://mlsanity.com/paper/2512.16832/) (2025) - [Language-Aware Prompt Tuning for Parameter-Efficient Seamless Language Expansion in Multilingual ASR](https://mlsanity.com/paper/2506.21577/) (2025) - [SAGE: Spliced-Audio Generated Data for Enhancing Foundational Models in Low-Resource Arabic-English Code-Switched Speech Recognition](https://mlsanity.com/paper/2506.22143/) (2025) - [Open Automatic Speech Recognition Models for Classical and Modern Standard Arabic](https://mlsanity.com/paper/2507.13977/) (2025) - [Doing More with Less: Data Augmentation for Sudanese Dialect Automatic Speech Recognition](https://mlsanity.com/paper/2601.06802/) (2026) - [LoASR-Bench: Evaluating Large Speech Language Models on Low-Resource Automatic Speech Recognition Across Language Families](https://mlsanity.com/paper/2603.20042/) (2026) - [Zero-Shot Context-Aware ASR for Diverse Arabic Varieties](https://mlsanity.com/paper/2511.18774/) (2025) - [BuzzASR: A Swarm of 100+ Monolingual Speech Recognition Models](https://mlsanity.com/paper/2609.09554/) (2026) - [CARDAMOM: A Micro-Dialectal Arabic Speech Dataset for ASR](https://mlsanity.com/paper/2609.34481/) (2026) ## Abstract (arXiv) Whisper, the recently developed multilingual weakly supervised model, is reported to perform well on multiple speech recognition benchmarks in both monolingual and multilingual settings. However, it is not clear how Whisper would fare under diverse conditions even on languages it was evaluated on such as Arabic. In this work, we address this gap by comprehensively evaluating Whisper on several varieties of Arabic speech for the ASR task. Our evaluation covers most publicly available Arabic speech data and is performed under n-shot (zero-, few-, and full) finetuning. We also investigate the robustness of Whisper under completely novel conditions, such as in dialect-accented standard Arabic and in unseen dialects for which we develop evaluation data. Our experiments show that although Whisper zero-shot outperforms fully finetuned XLS-R models on all datasets, its performance deteriorates significantly in the zero-shot setting for five unseen dialects (i.e., Algeria, Jordan, Palestine, UAE, and Yemen).