We present ArTST, a pre-trained Arabic text and speech transformer for
supporting open-source speech technologies for the Arabic language. The model
architecture follows the unified-modal framework, SpeechT5, that was recently
released for English, and is focused on Modern Standard Arabic (MSA), with
plans to extend the model for dialectal and code-switched Arabic in future
editions. We pre-trained the model from scratch on MSA speech and text data,
and fine-tuned it for the following tasks: Automatic Speech Recognition (ASR),
Text-To-Speech synthesis (TTS), and spoken dialect identification. In our
experiments comparing ArTST with SpeechT5, as well as with previously reported
results in these tasks, ArTST performs on a par with or exceeding the current
state-of-the-art in all three tasks. Moreover, we find that our pre-training is
conducive for generalization, which is particularly evident in the low-resource
TTS task. The pre-trained model as well as the fine-tuned ASR and TTS models
are released for research use.

我们提供了 ArTST，这是一个预先训练的阿拉伯文本和语音转换器，支持用于阿拉伯语言的开源语音技术。该模型的架构遵循最近发布的英语统一模态框架 SpeechT5，并且专注于现代标准阿拉伯语（MSA），未来版本计划将该模型扩展为方言和混合阿拉伯语。我们从头开始对 MSA 语音和文本数据进行了预训练，并对以下任务进行了微调：自动语音识别（ASR），文本到语音合成（TTS）和口语方言识别。在与 SpeechT5 以及先前报告的结果在这些任务中的比较实验中，ArTST 在所有三个任务中表现与或超过当前最先进技术水平。此外，我们发现我们的预训练有利于泛化，尤其在资源匮乏的 TTS 任务中表现明显。预训练模型以及微调后的 ASR 和 TTS 模型已发布供研究使用。