Data based filter design for RASTA-like channel normalization in ASR

RASTA processing has proven to be a successful technique for channel normalization in automatic speech recognition (ASR). We present two approaches to the design of RASTA like filters from training data. One consists of finding the solution to a constrained optimization problem on the feature time trajectories while the other uses linear discriminant analysis (LDA). Whereas LDA is often applied to one or a few frames of the feature vectors we apply LDA to feature time trajectories. Both approaches result in similar filters which are consistent with the ad hoc designed RASTA filter