This paper describes a system that gives a mobile robot the ability to perform automatic speech recognition with simultaneous speakers. A microphone array is used along with a real-time implementation of geometric source separation (GSS) and a postfilter that gives a further reduction of interference from other sources. The postfilter is also used to estimate the reliability of spectral features and compute a missing feature mask. The mask is used in a missing feature theory-based speech recognition system to recognize the speech from simultaneous Japanese speakers in the context of a humanoid robot. Recognition rates are presented for three simultaneous speakers located at 2 m from the robot. The system was evaluated on a 200-word vocabulary at different azimuths between sources, ranging from 10° to 90°. Compared to the use of the microphone array source separation alone, we demonstrate an average reduction in relative recognition error rate of 24% with the postfilter and of 42% when the missing features approach is combined with the postfilter. We demonstrate the effectiveness of our multisource microphone array postfilter and the improvement it provides when used in conjunction with the missing features theory.
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