  Humans are able to imagine a person's voice from the person's appearance and
imagine the person's appearance from his/her voice. In this paper, we make the
first attempt to develop a method that can convert speech into a voice that
matches an input face image and generate a face image that matches the voice of
the input speech by leveraging the correlation between faces and voices. We
propose a model, consisting of a speech converter, a face encoder/decoder and a
voice encoder. We use the latent code of an input face image encoded by the
face encoder as the auxiliary input into the speech converter and train the
speech converter so that the original latent code can be recovered from the
generated speech by the voice encoder. We also train the face decoder along
with the face encoder to ensure that the latent code will contain sufficient
information to reconstruct the input face image. We confirmed experimentally
that a speech converter trained in this way was able to convert input speech
into a voice that matched an input face image and that the voice encoder and
face decoder can be used to generate a face image that matches the voice of the
input speech.
