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A robust speech recognition system against the ego noise of a robot

This paper presents a speech recognition system for a mobile robot that attains a high recognition performance, even if the robot generates ego-motion noise. We investigate noise suppression and speech enhancement methods that are based on prediction of ego-motion and its noise. The estimation of ego-motion is used for superimposing white noise in a selective manner based on the ego-motion type. Moreover, instantaneous prediction of ego-motion noise is the core concept to estab-lish the following techniques: ego-motion noise suppression by template subtraction and missing feature theory based masking of noisy speech features. We evaluate the proposed technique on a robot using speech recognition results. Adaptive superimposition of white noise achieves up to 20% improvement of word correct rates (WCR) and the spectrographic mask attains an additional improvement of up to 10% compared to the single channel recognition.