  Speech recognition in noisy and channel distorted scenarios is often
challenging as the current acoustic modeling schemes are not adaptive to the
changes in the signal distribution in the presence of noise. In this work, we
develop a novel acoustic modeling framework for noise robust speech recognition
based on relevance weighting mechanism. The relevance weighting is achieved
using a sub-network approach that performs feature selection. A relevance
sub-network is applied on the output of first layer of a convolutional network
model operating on raw speech signals while a second relevance sub-network is
applied on the second convolutional layer output. The relevance weights for the
first layer correspond to an acoustic filterbank selection while the relevance
weights in the second layer perform modulation filter selection. The model is
trained for a speech recognition task on noisy and reverberant speech. The
speech recognition experiments on multiple datasets (Aurora-4, CHiME-3, VOiCES)
reveal that the incorporation of relevance weighting in the neural network
architecture improves the speech recognition word error rates significantly
(average relative improvements of 10% over the baseline systems)
