Adaptive Noise Reduction and Voice Activity Detection for improved Verbal Human-Robot Interaction using Binaural Data

Abstract

Speech has become an important part in human robot interaction (HRI), e.g. for person detection systems by using localized sound sources or for applications in automatic speech recognition (ASR) systems. By using speech in HRI in real world environments, we have to deal with mostly high and varying background noise, reverberation and also with different sound sources superimposing speech and other noises. Therefore, for real world scenarios a suitable signal preprocessing is essential. In this paper, we present a part of the artificial auditory system implemented on the mobile interaction robot HOROS using only two low cost microphones. We combined neural voice activity detection (VAD) and adaptive noise reduction which are essential aspects for HRI using mobile robot systems in changing and populated real-world environments. In the result, our system is able to robustly react on speech signals from its human interaction partner while ignoring other sound sources. Experiments show a significantly improved ASR performance in demanding environments making the system suitable for the use in real-world scenarios.