Entropy-based feature parameter weighting for robust speech recognition

In this work, we propose an entropy-based measure to determine the discriminating ability of a feature parameter in identifying the correct acoustic models, and a feature parameter weighting scheme using this measure during Viterbi decoding. The purpose is to emphasize the scores obtained with more discriminating parameters, and to de-emphasize the scores with less discriminating parameters. Extensive experiments verified that this approach is equally useful for different types of features, and can be easily integrated with typical existing robust speech recognition approaches.