  Lip-reading has made impressive progress in recent years, driven by advances
in deep learning. Nonetheless, the prerequisite such advances is a suitable
dataset. This paper provides a new in-the-wild dataset for Persian word-level
lipreading containing 244,000 videos from approximately 1,800 speakers. We
evaluated the state-of-the-art method in this field and used a novel approach
for word-level lip-reading. In this method, we used the AV-HuBERT model for
feature extraction and obtained significantly better performance on our
dataset.
