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.
Abstract (translated)
本文介绍了Mixat数据集：这是用英语对阿联酋语音进行混合的数据集。Mixat数据集是为了解决应用到阿联酋语音的现有语音识别资源的不足而开发的，尤其是针对双语阿联酋 speakers，他们经常混合和切换本地方言和英语。数据集包括来自两个公共播客的非母语阿联酋人士的15小时语音，其中一个是以主持人与嘉宾之间的对话形式呈现的。因此，数据集中包含了阿联酋-英语代码转换在正式和非正式会话背景中的例子。在本文中，我们描述了数据收集和注释的过程，并描述了数据集中的某些特征和统计数字。此外，我们还评估了预训练的阿拉伯语和多语言 ASR系统在我们的数据集上的性能，证明了对于这种低资源的中东阿拉伯语，现有模型的不足以及识别代码转换在 ASR 中的挑战。该数据集将公开发布，供研究使用。
URL
https://arxiv.org/abs/2405.02578
