# Mixat: A Data Set of Bilingual Emirati-English Speech

arXiv: https://arxiv.org/abs/2405.02578 · HTML: https://mlsanity.com/paper/2405.02578/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 13 references that are in the map.
- First posted on arXiv 4 May 2024.

## Builds on (most cited first, 4 of 4)

- [Robust Speech Recognition via Large-Scale Weak Supervision](https://mlsanity.com/paper/2212.04356/) (2022)
- [Scaling Speech Technology to 1,000+ Languages](https://mlsanity.com/paper/2305.13516/) (2023)
- [QASR: QCRI Aljazeera Speech Resource -- A Large Scale Annotated Arabic Speech Corpus](https://mlsanity.com/paper/2106.13000/) (2021)
- [ArTST: Arabic Text and Speech Transformer](https://mlsanity.com/paper/2310.16621/) (2023)

## Built on by (most cited first, 12 of 12)

- [OmniVoice: Towards Omnilingual Zero-Shot Text-to-Speech with Diffusion Language Models](https://mlsanity.com/paper/2604.00688/) (2026)
- [NADI 2025: The First Multidialectal Arabic Speech Processing Shared Task](https://mlsanity.com/paper/2509.02038/) (2025)
- [A Survey of Code-switched Arabic NLP: Progress, Challenges, and Future Directions](https://mlsanity.com/paper/2501.13419/) (2025)
- [Beyond Monolingual Assumptions: A Survey of Code-Switched NLP in the Era of Large Language Models across Modalities](https://mlsanity.com/paper/2510.07037/) (2025)
- [Code-Switching in End-to-End Automatic Speech Recognition: A Systematic Literature Review](https://mlsanity.com/paper/2507.07741/) (2025)
- [Arab Voices: Mapping Standard and Dialectal Arabic Speech Technology](https://mlsanity.com/paper/2601.13319/) (2026)
- [Abjad AI at NADI 2025: CATT-Whisper: Multimodal Diacritic Restoration Using Text and Speech Representations](https://mlsanity.com/paper/2510.24247/) (2025)
- [Habibi: Laying the Open-Source Foundation of Unified-Dialectal Arabic Speech Synthesis](https://mlsanity.com/paper/2601.13802/) (2026)
- [Ara-Best-RQ: Multi Dialectal Arabic SSL](https://mlsanity.com/paper/2603.21900/) (2026)
- [Linear Semantic Segmentation for Low-Resource Spoken Dialects](https://mlsanity.com/paper/2605.06276/) (2026)
- [Ramsa: A Large Sociolinguistically Rich Emirati Arabic Speech Corpus for ASR and TTS](https://mlsanity.com/paper/2603.08125/) (2026)
- [CARDAMOM: A Micro-Dialectal Arabic Speech Dataset for ASR](https://mlsanity.com/paper/2609.34481/) (2026)

## Abstract (arXiv)

This paper introduces Mixat: a dataset of Emirati speech code-mixed with English. Mixat was developed to address the shortcomings of current speech recognition resources when applied to Emirati speech, and in particular, to bilignual Emirati speakers who often mix and switch between their local dialect and English. The data set consists of 15 hours of speech derived from two public podcasts featuring native Emirati speakers, one of which is in the form of conversations between the host and a guest. Therefore, the collection contains examples of Emirati-English code-switching in both formal and natural conversational contexts. In this paper, we describe the process of data collection and annotation, and describe some of the features and statistics of the resulting data set. In addition, we evaluate the performance of pre-trained Arabic and multi-lingual ASR systems on our dataset, demonstrating the shortcomings of existing models on this low-resource dialectal Arabic, and the additional challenge of recognizing code-switching in ASR. The dataset will be made publicly available for research use.