---
title: "Bulbul: A Dataset for Dialectal Arabic Speech — Ashraf 2026"
description: "Arabic speech recognition struggles because speech resources are scarce and skewed toward a few varieties: MSA, Egyptian, broad Levantine, Gulf and…"
authors: ["Ahmed Ashraf", "Aisha Alansari", "Fadel Al Abbas", "Nada Almarwani", "Samah Aloufi", "Saad Ezzini", "Maged S. Al-Shaibani", "Doaa Dalaq", "AbdelRahim A. Elmadany", "Muhammad Abdul-Mageed", "Mohamed Mehdi Trigui", "Dania Refai", "Layan Refai", "Mohamed Akrout", "Mustafa Jarrar", "Wasfi G. Al-Khatib", "Alaa Dalaq", "Darin El-Nakla", "Samir Abdaljalil", "Abdulrahman Al-Fakih", "Nour El Imane Zeghib", "Moussa Redah", "Salmane Chafik", "Mohamed El-Attar", "Rima Grati", "Sarah Kohail", "Malak Alkhorasani", "Khadijah Al Safwan", "Ismail M. Mudhaffar", "Ali Altam", "Ahmed Al-Shaikh", "Adnan Saeed", "Hamzah Luqman"]
year: 2026
venue: "arXiv:2608.21950"
source: "https://arxiv.org/abs/2608.21950"
doi: "10.48550/arxiv.2608.21950"
arxiv: "2608.21950"
licence: "arXiv — non-exclusive license to distribute"
licence_url: "http://arxiv.org/licenses/nonexclusive-distrib/1.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/ashraf-2026-bulbul-dataset-dialectal-arabic-speech/"
---
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# Bulbul: A Dataset for Dialectal Arabic Speech Recognition

**Authors:** Ahmed Ashraf, [Aisha Alansari](https://md.research.elamur.ai/free/en/authors/aisha-alansari/), Fadel Al Abbas, [Nada Almarwani](https://md.research.elamur.ai/free/en/authors/nada-almarwani/), Samah Aloufi, [Saad Ezzini](https://md.research.elamur.ai/free/en/authors/saad-ezzini/), [Maged S. Al-Shaibani](https://md.research.elamur.ai/free/en/authors/maged-s-al-shaibani/), Doaa Dalaq, [AbdelRahim A. Elmadany](https://md.research.elamur.ai/free/en/authors/abdelrahim-a-elmadany/), [Muhammad Abdul-Mageed](https://md.research.elamur.ai/free/en/authors/muhammad-abdul-mageed/), Mohamed Mehdi Trigui, [Dania Refai](https://md.research.elamur.ai/free/en/authors/dania-refai/) et al. (33 authors) · **Year:** 2026 · **Venue:** arXiv:2608.21950 · **Licence:** [arXiv — non-exclusive license to distribute](http://arxiv.org/licenses/nonexclusive-distrib/1.0/)

**Links:** [Publisher's page](https://arxiv.org/abs/2608.21950) · [PDF](https://arxiv.org/pdf/2608.21950) · [DOI 10.48550/arxiv.2608.21950](https://doi.org/10.48550/arxiv.2608.21950) · [arXiv 2608.21950](https://arxiv.org/abs/2608.21950) · [OpenAlex](https://openalex.org/W7204192110)

**Citations:** 0 citations (OpenAlex, 2026-10-14) · 1 in our library, of the 140884 papers whose bibliographies we read

## Summary
Arabic speech recognition struggles because speech resources are scarce and skewed toward a few varieties: MSA, Egyptian, broad Levantine, Gulf and Moroccan. Dialects such as Algerian, Sudanese, Yemeni and Tunisian remain poorly covered. Accented MSA and Classical Arabic (CA) have no dedicated evaluation set. The authors built BULBUL, a community-collected corpus of about 72 hours from 275 volunteers in 11 countries, organized as 16 dialects. Saudi Arabia and Yemen are split into sub-dialects. Prompt texts came from public corpora, social media comments and donated private chats, which were anonymized before recording. Each country team leader recorded with a Gradio tool, and every clip went through two verification passes: the speaker checked it first, then another person checked it. The same participants also read fixed MSA and CA texts in their native accents. Benchmarks of recent ASR systems show large performance gaps across countries and dialects. Analyses of lexical overlap and speaking rate show that coarse dialect labels hide much internal variation. The resource gives a realistic, fairness-aware benchmark for underrepresented Arabic varieties.

## Key points
- 71.59 transcribed hours from 275 speakers (160 male, 115 female), collected between January 2025 and April 2026. Age and gender metadata are released.
- Countries covered:
  - Seven countries (Algeria, Egypt, Jordan, Morocco, Palestine, Sudan, Tunisia) use a neutralized "white dialect".
  - Syria uses Levantine, and the UAE uses the Abu Dhabi dialect.
  - Saudi Arabia has sub-dialects Hijazi, Najdi, Hijazi-Bedouin, Southern and Eastern (Eastern merges Qatifi and Hassawi). Yemen has Sana'ani and Ta'izzi.
- Text sources include Casablanca, SADA and MADAR, plus Facebook, X and YouTube comments and consented WhatsApp/Telegram chats. Accented MSA and CA prompts come from the Targamat dataset (200 CA and 200 MSA samples).
- Collection started with two countries (Saudi Arabia and Egypt) and expanded once the pipeline and validation were stable. Teams were coordinated through a Slack group and weekly meetings.
- Stated limitations:
  - Emotionally neutral, low-noise recordings.
  - Country and gender imbalance.
  - Partial sub-dialect coverage.
  - Small MSA/CA subsets.
  - A Palestinian subset concentrated in the economy domain.
- The dataset is available under a research-only, non-commercial license on request.

## Interesting ideas
- Speakers of different dialects read the same standardized MSA/CA texts, so content is held constant and only accent varies, which isolates one source of error. — For Mova (tree): Run one fixed set of sentences through each UD dialect conversion and compare parser scores, so differences can be attributed to the annotation convention rather than the text.
- Verification is layered: the producer checks their own output, then an independent reviewer checks it again before anything enters the corpus. — For Mova (learn): Silver trees could pass a self-consistency check by the producing parser before witness parsers vote, filtering cheap errors early.
- The authors warn that a topically narrow subset (Palestinian, mostly economy) may score better simply because its test data is homogeneous. — For Mova (answer): Report topical or lexical diversity next to each test suite's accuracy, so easy, homogeneous suites are not mistaken for real reader competence.
- Lexical overlap between subsets is used to explain performance gaps that coarse category labels hide. — For Mova (tokens): Measure vocabulary overlap between tagger training data and each evaluation genre to tell unknown-word failures apart from genuine morphology mistakes.
- The pipeline was proven on two countries before scaling to eleven, which kept procedures stable as the project grew. — For Mova (world): Compile knowledge (verb classes, measures, ontology links) for a couple of domains end to end before widening coverage, fixing the schema while changes are still cheap.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

Fields: [Linguistics & languages](https://md.research.elamur.ai/free/en/shelves/linguistics/)

Topics: [Speech & spoken language](https://md.research.elamur.ai/free/en/topics/speech/) · [Semitic languages](https://md.research.elamur.ai/free/en/topics/semitic-languages/)

## Cite

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@misc{ashraf2026bulbul,
  title = {{Bulbul: A Dataset for Dialectal Arabic Speech Recognition}},
  author = {Ashraf, Ahmed and Alansari, Aisha and Abbas, Fadel Al and Almarwani, Nada and Aloufi, Samah and Ezzini, Saad and Al-Shaibani, Maged S. and Dalaq, Doaa and Elmadany, AbdelRahim A. and Abdul-Mageed, Muhammad and Trigui, Mohamed Mehdi and Refai, Dania and Refai, Layan and Akrout, Mohamed and Jarrar, Mustafa and Al-Khatib, Wasfi G. and Dalaq, Alaa and El-Nakla, Darin and Abdaljalil, Samir and Al-Fakih, Abdulrahman and Zeghib, Nour El Imane and Redah, Moussa and Chafik, Salmane and El-Attar, Mohamed and Grati, Rima and Kohail, Sarah and Alkhorasani, Malak and Safwan, Khadijah Al and Mudhaffar, Ismail M. and Altam, Ali and Al-Shaikh, Ahmed and Saeed, Adnan and Luqman, Hamzah},
  year = {2026},
  doi = {10.48550/arxiv.2608.21950},
  eprint = {2608.21950},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.48550/arxiv.2608.21950}
}
```

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