  In this work, we present a novel audio-visual dataset for active speaker
detection in the wild. A speaker is considered active when his or her face is
visible and the voice is audible simultaneously. Although active speaker
detection is a crucial pre-processing step for many audio-visual tasks, there
is no existing dataset of natural human speech to evaluate the performance of
active speaker detection. We therefore curate the Active Speakers in the Wild
(ASW) dataset which contains videos and co-occurring speech segments with dense
speech activity labels. Videos and timestamps of audible segments are parsed
and adopted from VoxConverse, an existing speaker diarisation dataset that
consists of videos in the wild. Face tracks are extracted from the videos and
active segments are annotated based on the timestamps of VoxConverse in a
semi-automatic way. Two reference systems, a self-supervised system and a fully
supervised one, are evaluated on the dataset to provide the baseline
performances of ASW. Cross-domain evaluation is conducted in order to show the
negative effect of dubbed videos in the training data.
