  Audio-visual speaker recognition is one of the tasks in the recent 2019 NIST
speaker recognition evaluation (SRE). Studies in neuroscience and computer
science all point to the fact that vision and auditory neural signals interact
in the cognitive process. This motivated us to study a cross-modal network,
namely voice-face discriminative network (VFNet) that establishes the general
relation between human voice and face. Experiments show that VFNet provides
additional speaker discriminative information. With VFNet, we achieve 16.54%
equal error rate relative reduction over the score level fusion audio-visual
baseline on evaluation set of 2019 NIST SRE.
