A Robust Speech Enhancement System for Vehicular Applications Using H∞ Adaptive Filtering

This work proposes a novel and robust adaptive speech enhancement system, which contains both time-domain and frequency-domain beamformers using H_infin filtering approach in vehicle environments. A corresponding microphone array data acquisition hardware is also designed and implemented. Traditionally, mutually matched microphones are needed, but this requirement is not practical. To conquer this issue, the proposed system adapts the mismatch dynamics to allow unmatched microphones to be used in an array. Furthermore, to achieve a satisfactory speech recognition performance, the speech recognizer is usually required to be retrained for different vehicle environments due to different noise characteristics and channel effects. The channel effect usually causes the modeling error in a channel recovery process because of the long channel response. The proposed system using the H_infin filtering approach, which makes no assumptions about noise and disturbance, is robust to the modeling error. Consequently, the proposed frequency-domain beamformer provides a satisfactory performance without the need to retrain the speech.