Speech recognition based on space diversity using distributed multi-microphone

This paper proposes space diversity speech recognition technique using distributed multi-microphones in a room, as a new paradigm of speech recognition. The key technology to realize the system is (1) distant-talking speech recognition and (2) the integration method of multiple inputs. In this paper, we propose the use of a distant speech model for distant-talking speech recognition, and feature-based and likelihood-based integration methods for multimicrophones distributed in the room. The distant speech model is a set of HMMs learned using speech data convolved with the impulse responses measured at several points in the room. The experimental results of simulated distant-talking speech recognition show that the proposed space diversity speech recognition system can attain about 80% in accuracy, while the performances of conventional HMMs using close-talking microphones are less than 50%. These results indicate that the space diversity approach is promising for robust speech recognition under a real acoustic environment.