Automatic Speech Recognition for Indoor HRI Scenarios

Abstract

This article presents a stand-alone automatic speech recognition system that accounts for listener movement, time-varying reverberation effects, environmental noise, and user position information for beamforming approaches in an HRI setting. We raise the importance of replacing the classical black-box integration of automatic speech recognition technology in HRI applications with the incorporation of the acoustic environment representation and modeling, and of the target source direction. Test data were recorded on a real robot under various moving conditions. For addressing the time-varying acoustic channel problem and incorporating environmental effect during training, clean speech samples were passed through estimated static channel responses and noise was added. Beamforming is investigated regarding oracle source tracking using, for instance, image processing. The proposed strategy is interesting for the robotics community, because it allows the development of voice-based HRI with limited training data and without relying on third-party technologies or Internet access eliminating the need to upload data to the cloud. In our mobile HRI scenario, the resulting speech recognition engine provided an average word error rate that is at least 19% and 34% lower than publicly available speech recognition APIs with the playback (i.e., loudspeaker) and human testing modalities, respectively.