We describe use of Linear Discriminant Analysis (LDA) for data-driven automatic design of RASTA-like filters. The LDA applied to rather long segments of time trajectories of critical-band energies yields FIR filters to be applied to these time trajectories in the feature extraction module. Frequency responses of the first three discriminant vectors are in principle consistent with the ad hoc designed RASTA, delta and double-delta filters. On a connected digit task the new features outperform the original RASTA processing.