Automatic Speech Recognition Under Ego-motion Noise of a Robot

Active auditory perception related tasks like sound localization and speech recognition have to be performed with high accuracy even while the robot is moving. However, the joints of the robot inevitably generate noise because of the active motors, i.e. ego-motion noise. This problem is very critical, especially in humanoid robots, because they tend to have a lot of joints and the motors are located relatively closer to the microphones than the sound sources. In this work, we investigate methods for the prediction and suppression of the ego-motion noise. In the first part, we analyze the performance of different noise subtraction strategies, assuming that the noise prediction problem has been solved. In the second part, we present some results for a noise prediction scheme based on the current robot joint status. Performance is evaluated for a number of criteria, including Automatic Speech Recognition (ASR). We demonstrate that our method improves recognition performance during ego-motion considerably.