  Speech is a rich biometric signal that contains information about the
identity, gender and emotional state of the speaker. In this work, we explore
its potential to generate face images of a speaker by conditioning a Generative
Adversarial Network (GAN) with raw speech input. We propose a deep neural
network that is trained from scratch in an end-to-end fashion, generating a
face directly from the raw speech waveform without any additional identity
information (e.g reference image or one-hot encoding). Our model is trained in
a self-supervised approach by exploiting the audio and visual signals naturally
aligned in videos. With the purpose of training from video data, we present a
novel dataset collected for this work, with high-quality videos of youtubers
with notable expressiveness in both the speech and visual signals.
