  This report describes our approach for the Audio-Visual Diarization (AVD)
task of the Ego4D Challenge 2022. Specifically, we present multiple technical
improvements over the official baselines. First, we improve the detection
performance of the camera wearer's voice activity by modifying the training
scheme of its model. Second, we discover that an off-the-shelf voice activity
detection model can effectively remove false positives when it is applied
solely to the camera wearer's voice activities. Lastly, we show that better
active speaker detection leads to a better AVD outcome. Our final method
obtains 65.9% DER on the test set of Ego4D, which significantly outperforms all
the baselines. Our submission achieved 1st place in the Ego4D Challenge 2022.
