  The goal of this work is to determine 'who spoke when' in real-world
meetings. The method takes surround-view video and single or multi-channel
audio as inputs, and generates robust diarisation outputs. To achieve this, we
propose a novel iterative approach that first enrolls speaker models using
audio-visual correspondence, then uses the enrolled models together with the
visual information to determine the active speaker. We show strong quantitative
and qualitative performance on a dataset of real-world meetings. The method is
also evaluated on the public AMI meeting corpus, on which we demonstrate
results that exceed all comparable methods. We also show that beamforming can
be used together with the video to further improve the performance when
multi-channel audio is available.
