Improving Audiovisual Active Speaker Detection in Egocentric Recordings with the Data-Efficient Image Transformer

Future augmented reality devices have the capacity to enhance human perception and provide assistive functions in complex communication scenarios. Active speaker detection (ASD) systems that are robust to egocentric data are critical to this. Egocentric ASD is challenging due to overlapping speech, single-channel recording, and dynamic scenes. A novel module that uses a data-efficient image transformer (DeiT) to extract features encapsulating the acoustic properties of each scene, and a positional conditioning mechanism is proposed. The module is evaluated in conjunction with TalkNet, an existing ASD architecture, on two audiovisual datasets: Ego4D (egocentric) and AVA-ActiveSpeaker (exocentric), achieving 29% and 0.38% relative improvement in mean Average Precision (mAP), respectively, while retaining a parameter efficient build. A qualitative analysis is also presented, implicitly demonstrating that contextual information is leveraged.