  We introduce a seemingly impossible task: given only an audio clip of someone
speaking, decide which of two face images is the speaker. In this paper we
study this, and a number of related cross-modal tasks, aimed at answering the
question: how much can we infer from the voice about the face and vice versa?
We study this task "in the wild", employing the datasets that are now publicly
available for face recognition from static images (VGGFace) and speaker
identification from audio (VoxCeleb). These provide training and testing
scenarios for both static and dynamic testing of cross-modal matching. We make
the following contributions: (i) we introduce CNN architectures for both binary
and multi-way cross-modal face and audio matching, (ii) we compare dynamic
testing (where video information is available, but the audio is not from the
same video) with static testing (where only a single still image is available),
and (iii) we use human testing as a baseline to calibrate the difficulty of the
task. We show that a CNN can indeed be trained to solve this task in both the
static and dynamic scenarios, and is even well above chance on 10-way
classification of the face given the voice. The CNN matches human performance
on easy examples (e.g. different gender across faces) but exceeds human
performance on more challenging examples (e.g. faces with the same gender, age
and nationality).
