  We provide the technical report for Ego4D audio-only diarization challenge in
ECCV 2022. Speaker diarization takes the audio streams as input and outputs the
homogeneous segments according to the speaker's identity. It aims to solve the
problem of "Who spoke when." In this paper, we explore a Detection-based method
to tackle the audio-only speaker diarization task. Our method first extracts
audio features by audio backbone and then feeds the feature to a
detection-generate network to get the speaker proposals. Finally, after
postprocessing, we can get the diarization results. The validation dataset
validates this method, and our method achieves 53.85 DER on the test dataset.
These results rank 3rd on the leaderboard of Ego4D audio-only diarization
challenge 2022.
