Speech-based Human-Robot Interaction Robust to Acoustic Reflections in Real Environment

Acoustic reflection inside an enclosed environment is detrimental to human-robot interaction. Reflection may manifest as phantom sources emanating from unknown directions. In effect, a single speaker may falsely manifest as multiple speakers to the robot audition system, impeding the robot's ability to correctly associate the speech command to the actual speaker. Moreover, speech reflection smears the original speech signal due to reverberation. This degrades speech recognition and understanding performance. Conventional robot audition schemes that rely purely on acoustics and spatial information are very sensitive to acoustic reflection which ultimately leads to the failure in human-robot interaction. We propose a method for human-robot interaction robust to the effect of acoustic reflection. First, visual information is utilized and head tracking scheme is employed to reinforce the acoustic information with the visual presence of a prospect user. Second, we employ a model-based sound event identification scheme and scrutinize whether the acoustic information is likely to be speech or non-speech. Using all the information we have gathered, we create a simple rule construct to effectively discriminate the original source (actual speaker) from phantom sources (reflection). Consequently, the corresponding source identified as phantom (reflection) is used to estimate the unwanted smearing for effective suppression via speech enhancement. Experiments are conducted in human-robot interaction setting in which the proposed method outperforms the conventional method.