Noise robust speech recognition using recent developments in neural networks for computer vision

Convolutional Neural Networks (CNNs) are superior to fully connected neural networks in various speech recognition tasks and the advantage is pronounced in noisy environments. In recent years, many techniques have been proposed in the computer vision community to improve CNN's classification performance. This paper considers two approaches recently developed for image classification and examines their impacts on noisy speech recognition performance. The first approach is to increase the depth of convolution layers. Different approaches to deepening the CNNs are compared. In particular, the usefulness of learning dynamic features with small convolution layers that perform convolution in time is shown along with a modulation frequency analysis of the learned convolution filters. The second approach is to use trainable activation functions. Specifically, the use of a Parametric Rectified Linear Unit (PReLU) is investigated. Experimental results show that both approaches yield significant improvements in performance. Combining the two approaches further reduces recognition errors, producing a word error rate of 11.1% in the Aurora4 task, the best published result for this corpus, with a standard one-pass bi-gram decoding set-up.