Ego noise cancellation of a robot using missing feature masks

We describe an architecture that gives a robot the capability to recognize speech by cancelling ego noise, even while the robot is moving. The system consists of three blocks: (1) a multi-channel noise reduction block, comprising consequent stages of microphone-array-based sound localization, geometric source separation and post-filtering; (2) a single-channel noise reduction block utilizing template subtraction; and (3) an automatic speech recognition block. In this work, we specifically investigate a missing feature theory-based automatic speech recognition (MFT-ASR) approach in block (3). This approach makes use of spectro-temporal elements derived from (1) and (2) to measure the reliability of the acoustic features, and generates masks to filter unreliable acoustic features. We then evaluated this system on a robot using word correct rates. Furthermore, we present a detailed analysis of recognition accuracy to determine optimal parameters. Implementation of the proposed MFT-ASR approach resulted in significantly higher recognition performance than single or multi-channel noise reduction methods.