Time-varying noise compensation using multiple Kalman filters

The environmental conditions in which a speech recognition system should be operating are usually nonstationary. We present an approach to compensate for the effects of time-varying noise using a bank of Kalman filters. The presented method is based on the interacting multiple model (IMM) technique well-known in the area of multiple target tracking. Moreover, we propose a way to get fixed-interval smoothed estimates for the environmental parameters. The performances of the proposed approaches are evaluated in the continuous digit recognition experiments where not only the slowly evolving noise but also the rapidly varying noise sources are added to simulate the noisy environments