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

TL;DR

This paper presents a self-supervised reinforcement learning framework enabling the iCub robot to autonomously learn to localize speakers by mimicking human developmental mechanisms, improving speech recognition in noisy environments.

Contribution

It introduces a novel self-supervised reinforcement learning approach for speaker localization on the iCub robot, inspired by human development, without requiring labeled datasets.

Findings

Robot successfully localizes speakers in noisy environments.

Self-supervised learning reduces the need for labeled data.

Improved speech recognition performance in challenging conditions.

Abstract

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.