  Speaker diarization is well studied for constrained audios but little
explored for challenging in-the-wild videos, which have more speakers, shorter
utterances, and inconsistent on-screen speakers. We address this gap by
proposing an audio-visual diarization model which combines audio-only and
visual-centric sub-systems via late fusion. For audio, we show that an
attractor-based end-to-end system (EEND-EDA) performs remarkably well when
trained with our proposed recipe of a simulated proxy dataset, and propose an
improved version, EEND-EDA++, that uses attention in decoding and a speaker
recognition loss during training to better handle the larger number of
speakers. The visual-centric sub-system leverages facial attributes and
lip-audio synchrony for identity and speech activity estimation of on-screen
speakers. Both sub-systems surpass the state of the art (SOTA) by a large
margin, with the fused audio-visual system achieving a new SOTA on the AVA-AVD
benchmark.
