  Human beings have developed fantastic abilities to integrate information from
various sensory sources exploring their inherent complementarity. Perceptual
capabilities are therefore heightened, enabling, for instance, the well-known
"cocktail party" and McGurk effects, i.e., speech disambiguation from a panoply
of sound signals. This fusion ability is also key in refining the perception of
sound source location, as in distinguishing whose voice is being heard in a
group conversation. Furthermore, neuroscience has successfully identified the
superior colliculus region in the brain as the one responsible for this
modality fusion, with a handful of biological models having been proposed to
approach its underlying neurophysiological process. Deriving inspiration from
one of these models, this paper presents a methodology for effectively fusing
correlated auditory and visual information for active speaker detection. Such
an ability can have a wide range of applications, from teleconferencing systems
to social robotics. The detection approach initially routes auditory and visual
information through two specialized neural network structures. The resulting
embeddings are fused via a novel layer based on the superior colliculus, whose
topological structure emulates spatial neuron cross-mapping of unimodal
perceptual fields. The validation process employed two publicly available
datasets, with achieved results confirming and greatly surpassing initial
expectations.
