  In this paper, we study the associations between human faces and voices.
Audiovisual integration, specifically the integration of facial and vocal
information is a well-researched area in neuroscience. It is shown that the
overlapping information between the two modalities plays a significant role in
perceptual tasks such as speaker identification. Through an online study on a
new dataset we created, we confirm previous findings that people can associate
unseen faces with corresponding voices and vice versa with greater than chance
accuracy. We computationally model the overlapping information between faces
and voices and show that the learned cross-modal representation contains enough
information to identify matching faces and voices with performance similar to
that of humans. Our representation exhibits correlations to certain demographic
attributes and features obtained from either visual or aural modality alone. We
release our dataset of audiovisual recordings and demographic annotations of
people reading out short text used in our studies.
