  Recognizing who is speaking in a crowded scene is a key challenge towards the
understanding of the social interactions going on within. Detecting speaking
status from body movement alone opens the door for the analysis of social
scenes in which personal audio is not obtainable. Video and wearable sensors
make it possible recognize speaking in an unobtrusive, privacy-preserving way.
When considering the video modality, in action recognition problems, a bounding
box is traditionally used to localize and segment out the target subject, to
then recognize the action taking place within it. However, cross-contamination,
occlusion, and the articulated nature of the human body, make this approach
challenging in a crowded scene. Here, we leverage articulated body poses for
subject localization and in the subsequent speech detection stage. We show that
the selection of local features around pose keypoints has a positive effect on
generalization performance while also significantly reducing the number of
local features considered, making for a more efficient method. Using two
in-the-wild datasets with different viewpoints of subjects, we investigate the
role of cross-contamination in this effect. We additionally make use of
acceleration measured through wearable sensors for the same task, and present a
multimodal approach combining both methods.
