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

Intel Labs at Ego4D Challenge 2022: A Better Baseline for Audio-Visual Diarization