  This report introduces our novel method named STHG for the Audio-Visual
Diarization task of the Ego4D Challenge 2023. Our key innovation is that we
model all the speakers in a video using a single, unified heterogeneous graph
learning framework. Unlike previous approaches that require a separate
component solely for the camera wearer, STHG can jointly detect the speech
activities of all people including the camera wearer. Our final method obtains
61.1% DER on the test set of Ego4D, which significantly outperforms all the
baselines as well as last year's winner. Our submission achieved 1st place in
the Ego4D Challenge 2023. We additionally demonstrate that applying the
off-the-shelf speech recognition system to the diarized speech segments by STHG
produces a competitive performance on the Speech Transcription task of this
challenge.
