Rectifier nonlinearities improve neural network acoustic models

Abstract

Deep neural network acoustic models produce substantial gains in large vocabulary continuous speech recognition systems. Emerging work with rectified linear (ReL) hidden units demonstrates additional gains in final system performance relative to more commonly used sigmoidal nonlinearities. In this work, we explore the use of deep rectifier networks as acoustic models for the 300 hour Switchboard conversational speech recognition task. Using simple training procedures without pretraining, networks with rectifier nonlinearities produce 2% absolute reductions in word error rates over their sigmoidal counterparts. We analyze hidden layer representations to quantify differences in how ReL units encode inputs as compared to sigmoidal units. Finally, we evaluate a variant of the ReL unit with a gradient more amenable to optimization in an attempt to further improve deep rectifier networks.