  In the future robots will interact more and more with humans and will have to
communicate naturally and efficiently. Automatic speech recognition systems
(ASR) will play an important role in creating natural interactions and making
robots better companions. Humans excel in speech recognition in noisy
environments and are able to filter out noise. Looking at a person's face is
one of the mechanisms that humans rely on when it comes to filtering speech in
such noisy environments. Having a robot that can look toward a speaker could
benefit ASR performance in challenging environments. To this aims, we propose a
self-supervised reinforcement learning-based framework inspired by the early
development of humans to allow the robot to autonomously create a dataset that
is later used to learn to localize speakers with a deep learning network.
