  Successful active speaker detection requires a three-stage pipeline: (i)
audio-visual encoding for all speakers in the clip, (ii) inter-speaker relation
modeling between a reference speaker and the background speakers within each
frame, and (iii) temporal modeling for the reference speaker. Each stage of
this pipeline plays an important role for the final performance of the created
architecture. Based on a series of controlled experiments, this work presents
several practical guidelines for audio-visual active speaker detection.
Correspondingly, we present a new architecture called ASDNet, which achieves a
new state-of-the-art on the AVA-ActiveSpeaker dataset with a mAP of 93.5%
outperforming the second best with a large margin of 4.7%. Our code and
pretrained models are publicly available.
