Ava Active Speaker: An Audio-Visual Dataset for Active Speaker Detection

Abstract

Active speaker detection is an important component in video analysis algorithms for applications such as speaker diarization, video re-targeting for meetings, speech enhancement, and human-robot interaction. The absence of a large, carefully labeled audio-visual active speaker dataset has limited evaluation in terms of data diversity, environments, and accuracy. In this paper, we present the AVA Active Speaker detection dataset (AVA-ActiveSpeaker) which has been publicly released to facilitate algorithm development and comparison. It contains temporally labeled face tracks in videos, where each face instance is labeled as speaking or not, and whether the speech is audible. The dataset contains about 3.65 million human labeled frames spanning 38.5 hours. We also introduce a state-of-the-art, jointly trained audio-visual model for real-time active speaker detection and compare several variants. The evaluation clearly demonstrates a significant gain due to audio-visual modeling and temporal integration over multiple frames.