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.

论文介绍了Mixat数据集，该数据集是阿拉伯语和多语言的语音识别系统在阿联酋方言的表现及其代码切换识别方面存在的不足的解决方案之一。数据集包括15小时的两个公共播客中，以阿拉伯语和英语进行代码混合的阿联酋演讲的例子，可以应用于语音识别的研究。

Mixat: 阿联酋英语双语语音数据集