  Although speaker verification has conventionally been an audio-only task,
some practical applications provide both audio and visual streams of input. In
these cases, the visual stream provides complementary information and can often
be leveraged in conjunction with the acoustics of speech to improve
verification performance. In this study, we explore audio-visual approaches to
speaker verification, starting with standard fusion techniques to learn joint
audio-visual (AV) embeddings, and then propose a novel approach to handle
cross-modal verification at test time. Specifically, we investigate unimodal
and concatenation based AV fusion and report the lowest AV equal error rate
(EER) of 0.7% on the VoxCeleb1 dataset using our best system. As these methods
lack the ability to do cross-modal verification, we introduce a multi-view
model which uses a shared classifier to map audio and video into the same
space. This new approach achieves 28% EER on VoxCeleb1 in the challenging
testing condition of cross-modal verification.
