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

Self-supervised reinforcement learning for speaker localisation with the iCub humanoid robot