  We address the problem of active speaker detection through a new framework,
called SPELL, that learns long-range multimodal graphs to encode the
inter-modal relationship between audio and visual data. We cast active speaker
detection as a node classification task that is aware of longer-term
dependencies. We first construct a graph from a video so that each node
corresponds to one person. Nodes representing the same identity share edges
between them within a defined temporal window. Nodes within the same video
frame are also connected to encode inter-person interactions. Through extensive
experiments on the Ava-ActiveSpeaker dataset, we demonstrate that learning
graph-based representation, owing to its explicit spatial and temporal
structure, significantly improves the overall performance. SPELL outperforms
several relevant baselines and performs at par with state of the art models
while requiring an order of magnitude lower computation cost.
