This correspondence reports results for a tracking, real-time microphone array as an input to a hidden Markov model based (HMM-based) connected alpha-digits speech recognizer. For a talker in the near field of the array (within 0.5 m), performance approaches that of a close-talking microphone input device.

A major problem for speech recognition systems is relieving the talker of the need to use a close-talking, head-mounted or a deskstand microphone. A likely solution is the use of an array of microphones that can steer itself to the talker and can use a beamforming algorithm to overcome the reduced signal-to-noise ratio due to room acoustics. This paper reports results for a tracking, real-time microphone-array as an input to an HMM-based connected alpha-digits speech recognizer. For a talker in the very near field of the array (within a meter), performance approaches that of a close-talking microphone input device. The effects of both the noise reducing steered array and the use of a maximum a posteriori (MAP) training step are shown to be significant. Here, the array system and the recognizer are described, experiments are presented, and the implications of combining these two systems discussed.