Mixat: A Data Set of Bilingual Emirati-English Speech

TL;DR

Mixat is a new dataset of Emirati-English speech capturing code-switching, designed to improve speech recognition for this low-resource dialect and bilingual context, highlighting current model limitations.

Contribution

The paper introduces Mixat, a novel bilingual Emirati-English speech dataset with annotations, addressing the lack of resources for dialectal Arabic and code-switching recognition.

Findings

Existing ASR models perform poorly on Mixat data.

The dataset reveals challenges in recognizing code-switching.

Mixat will be publicly available for research.

Abstract

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.