Evaluation of multisensor speech input for speech recognition in high ambient noise

In this paper, we report the results of isolated-word speech recognition tests performed with the Verbex 4000 recognizer on speech data collected 1) in 95 dB and 115 dB SPL broad-band acoustic noise typical in a fighter aircraft cockpit and 2) using several single sensors and two-sensor configurations that we reported in ICASSP-85. The two-sensor systems and the gradient microphones we tested produced about the same recognition performance in 95 dB noise. In 115 dB noise, the recognizer failed to train for all but the accelerometer, which produced nearly constant performance in both noise levels. Also, we demonstrate the feasibility of a new speech recognition methodology that uses a parallel system of multiple input signals transduced simultaneously using different sensors. In selected phonetic discrimination tests involving a feature-based approach, a parallel-input multisensor system reduced the discrimination errors to between one-half and one-twelfth of the number produced by a gradient microphone alone.