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Abstract : |
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., |
