riotu-lab/ARCADE-full

ARCADE: Arabic Radio Corpus for Audio Dialect Evaluation

ARCADE is a city-scale corpus of Arabic radio speech designed for fine-grained dialect identification. The dataset contains 6,907 annotations for 3,790 unique audio segments collected from radio streams spanning 58 cities across 19 Arab countries.

Dataset Description

Each 30-second audio clip is annotated with:

• City and Country: Fine-grained geographic labels at the city level
• MSA or Dialect: Whether the speech is Modern Standard Arabic, dialectal, mixed, or not applicable
• Emotion: Speaker emotion (neutral, happiness, anger, etc.)
• Audio Type: Single speaker, multiple speakers, music/no speech, or Quran recitation
• Keep or Skip: Whether the clip is suitable for dialect modeling
• Confidence: Annotator confidence level (sure, unsure, no idea)

The filename alone is not a unique identifier and may appear across multiple cities. To obtain a unique key, concatenate the filename with the corresponding city name.

A detailed description of the dataset is provided in the accompanying paper: https://arxiv.org/abs/2601.02209

Intended Uses

• Fine-grained Arabic dialect identification at the city level
• Sociolinguistic studies of regional speech variation
• Multi-task learning combining dialect, emotion, and speaker classification
• Robustness evaluation under domain and channel shift

Dataset Statistics

• Total annotations: 6,907
• Total unique audio segments: 3,790
• Cities: 58
• Countries: 19
• Clip duration: 30 seconds

Citation

If you use this dataset, please cite:

@misc{nacar2026arcadecityscalecorpusfinegrained,
      title={ARCADE: A City-Scale Corpus for Fine-Grained Arabic Dialect Tagging}, 
      author={Omer Nacar and Serry Sibaee and Adel Ammar and Yasser Alhabashi and Nadia Samer Sibai and Yara Farouk Ahmed and Ahmed Saud Alqusaiyer and Sulieman Mahmoud AlMahmoud and Abdulrhman Mamdoh Mukhaniq and Lubaba Raed and Sulaiman Mohammed Alatwah and Waad Nasser Alqahtani and Yousif Abdulmajeed Alnasser and Mohamed Aziz Khadraoui and Wadii Boulila},
      year={2026},
      eprint={2601.02209},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2601.02209}, 
}

📄 Paper: arXiv:2601.02209

License

This dataset is released under the CC BY 4.0 license for non-commercial academic and research use.