A hybrid framework for ego noise cancellation of a robot

Noise generated due to the motion of a robot is not desired, because it deteriorates the quality and intelligibility of the sounds recorded by robot-embedded microphones. It must be reduced or cancelled to achieve automatic speech recognition with a high performance. In this work, we divide ego-motion noise problem into three subdomains of arm, leg and head motion noise, depending on their complexity and intensity levels. We investigate methods that make use of single-channel and multi-channel processing in order to suppress ego noise separately. For this purpose, a framework consisting of a microphone-array-based geometric source separation, a consequent post filtering process and a parallel module for template subtraction is used. Furthermore, a control mechanism is proposed, which is based on signal-to-noise ratio and instantaneously detected motions, to switch to the most suitable method to deal with the current type of noise. We evaluate the proposed techniques on a humanoid robot using automatic speech recognition (ASR). The preliminary results of isolated word recognition show the effectiveness of our methods by increasing the word correct rates up to 50% compared to the single channel recognition in arm and leg motion noises and up to 25% in very strong head motion noises.