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. Copyright © 2023, The Authors. All rights reserved.
Arabic languages, Arabic speech, Arabic texts, Automatic speech recognition, Modeling architecture, Modern standards, Open-source, Speech data, Speech technology, Standard arabics
H.O. Toyin, A. Djanibekov, A. Kulkarni, and H. Aldarmaki, "ArTST: Arabic Text and Speech Transformer", arXiv, Oct 2023. doi:10.48550/arXiv.2310.16621