Minimum variance modulation filter for robust speech recognition

This paper describes a way of designing modulation filter by data driven analysis which improves the performance of automatic speech recognition systems that operate in real environments. The filter for each nonlinear channel output is obtained by a constrained optimization process which jointly minimizes the environmental distortion as well as the distortion caused by the filter itself. Recognition accuracy is measured using the CMU SPHINX-III speech recognition system, and the DARPA resource management and Wall Street Journal speech corpus for training and testing. It is shown that feature extraction followed by modulation filtering provides better performance than traditional MFCC processing under different types of background noise and reverberation.