---
dataset_info:
  features:
  - name: segment_id
    dtype: string
  - name: audio
    dtype:
      audio:
        sampling_rate: 16000
  - name: dialect
    dtype: string
  - name: domain
    dtype: string
  - name: audio_duration
    dtype: float64
  splits:
  - name: test
    num_bytes: 1354672655.25
    num_examples: 4854
  download_size: 1338284576
  dataset_size: 1354672655.25
configs:
- config_name: default
  data_files:
  - split: test
    path: data/test-*
license: cc
task_categories:
- audio-classification
language:
- ar
tags:
- dialect
pretty_name: 'MADIS 5: Multi-domain Arabic Dialect Identification in Speech'
size_categories:
- 1K<n<10K
---

<div align="center">
<img src="assets/madis_logo.png" alt="MADIS-5 Logo" width="600">
</div>


## Dataset Overview

**MADIS-5** (**M**ulti-domain **A**rabic **D**ialect **I**dentification in **S**peech) is a manually curated dataset 
designed to facilitate evaluation of cross-domain robustness of Arabic Dialect Identification (ADI) systems.
This dataset provides a comprehensive benchmark for testing out-of-domain generalization across different speech domains with diverse recording conditions and speaking styles.


## Dataset Statistics

* **Total Duration**: ~12 hours of speech
* **Total Utterances**: 4,854 utterances
* **Languages/Dialects**: 5 major Arabic varieties
    * Modern Standard Arabic (MSA)
    * Egyptian Arabic
    * Gulf Arabic
    * Levantine Arabic
    * Maghrebi Arabic
* **Domains**: 4 different spoken domains
* **Collection Period**: November 2024 - Feb 2025

## Data Sources

Our dataset comprises speech samples from four different public sources, each offering varying degrees of similarity to the TV broadcast domain commonly used in ADI research:

### 📻 **Radio Broadcasts**
- **Source**: Local radio stations across the Arab world via radio.garden
- **Characteristics**: Similar to prior ADI datasets but with more casual, spontaneous speech
- **Domain Similarity**: High similarity to existing ADI benchmarks

### 📺 **TV Dramas** 
- **Source**: Arabic Spoken Dialects Regional Archive ([SARA](https://www.kaggle.com/datasets/murtadhayaseen/arabic-spoken-regional-archive-sara)) on Kaggle
- **Characteristics**: 5-7 second conversational speech segments
- **Domain Similarity**: Low similarity with more dialogues

### 🎤 **TEDx Talks**
- **Source**: Arabic portion of the [TEDx dataset](https://www.openslr.org/100) with dialect labels 
- **Characteristics**: Presentations with educational content
- **Domain Similarity**: Moderate similarity due to topic diversity

### 🎭 **Theater**
- **Source**: YouTube dramatic and comedy plays from various Arab countries
- **Characteristics**: Theatrical performances spanning different time periods
- **Domain Similarity**: Low similarity with artistic and performative speech, with occasional poor recording conditions 

## Annotation Process

### Quality Assurance
- **Primary Annotator**: Native Arabic speaker with PhD in Computational Linguistics and extensive exposure to Arabic language variation
- **Verification**: Independent verification by a second native Arabic speaker with expertise in Arabic dialects
- **Segmentation**: Manual segmentation and labeling of all recordings

### Inter-Annotator Agreement
- **Perfect Agreement**: 97.7% of all samples
- **Disagreement**: 2.3% disagreement on radio broadcast segments (MSA vs. dialect classification)
- **Note**: The small disagreement reflects the natural continuum between MSA and dialectal Arabic in certain contexts.
  Final label of segments with disagreement was assigned after a discussion between annotators. 


## Use Cases

This dataset is ideal for:

- **Cross-domain robustness evaluation** of Arabic dialect identification systems
- **Benchmarking** ADI models across diverse speech domains
- **Research** on domain adaptation in Arabic speech processing
- **Development** of more robust Arabic dialect classifiers

## Dataset Advantages

- **Domain Diversity**: Four distinct speech domains with varying recording conditions
- **Expert Annotation**: High-quality labels from linguistic experts
- **Cross-domain Focus**: Specifically designed to test model robustness beyond single domains
- **Real-world Scenarios**: Covers authentic speech from various contexts

## Citation

If you use this dataset in your research, please cite our paper:

```bibtex
@inproceedings{abdullah2025voice,
  title={Voice Conversion Improves Cross-Domain Robustness for Spoken Arabic Dialect Identification},
  author={Abdullah, Badr M. and Matthew Baas and Bernd Möbius and Dietrich Klakow},
  year={2025},
  publisher={Interspeech},
  url={arxiv.org/abs/2505.24713}
}
```

## License

Creative Commons Attribution-NonCommercial-NoDerivs 4.0 (CC BY-NC-ND 4.0) 

## Acknowledgments

We thank the contributors to the source datasets and platforms that made this compilation possible, including radio.garden, SARA archive, and the Multilingual TEDx dataset.