  In this paper, we address the problem of lip-voice synchronisation in videos
containing human face and voice. Our approach is based on determining if the
lips motion and the voice in a video are synchronised or not, depending on
their audio-visual correspondence score. We propose an audio-visual cross-modal
transformer-based model that outperforms several baseline models in the
audio-visual synchronisation task on the standard lip-reading speech benchmark
dataset LRS2. While the existing methods focus mainly on lip synchronisation in
speech videos, we also consider the special case of the singing voice. The
singing voice is a more challenging use case for synchronisation due to
sustained vowel sounds. We also investigate the relevance of lip
synchronisation models trained on speech datasets in the context of singing
voice. Finally, we use the frozen visual features learned by our lip
synchronisation model in the singing voice separation task to outperform a
baseline audio-visual model which was trained end-to-end. The demos, source
code, and the pre-trained models are available on
https://ipcv.github.io/VocaLiST/
