A portable robot audition software system for multiple simultaneous speech signals

Since a robot is deployed in various kinds of environments, the robot audition system should work with minimum prior information on environments to localize, separate and recognize utterances by multiple simultaneous talkers. For example, it should not assume either the number of speakers, the location of speakers for sound source separation (SSS), or specially tuned acoustic model for automatic speech recognition (ASR). We developed \HARK" portable robot audition that uses eight microphones installed on the surface of robot's body such as Honda ASIMO, and SIG-2 and Robovie-R2 at Kyoto University. HARK integrates SSS and ASR by using the Missing-Feature Theory. For SSS, we use Geometric Source Separation and multi-channel post-filter to separate each utterance. Since separated speech signals are distorted due to interfering talkers and sound source separation, multi-channel post-filter enhanced speech signals. At this process, we create a missing feature mask that specifies which acoustic features are reliable in time-frequency domain. Multi-band Julius, a missing-feature-theory based ASR, uses this mask to avoid the inuence of unreliable features in recognizing such distorted speech signals. The system demonstrated a waitress robot that accepts meal orders placed by three actual human talkers.