An investigation of deep neural networks for noise robust speech recognition

The noise robustness of DNN-based acoustic models can match state-of-the-art performance on the Aurora 4 task without any explicit noise compensation and can be further improved by incorporating information about the environment into DNN training using a new method called noise-aware training.

Abstract

Recently, a new acoustic model based on deep neural networks (DNN) has been introduced. While the DNN has generated significant improvements over GMM-based systems on several tasks, there has been no evaluation of the robustness of such systems to environmental distortion. In this paper, we investigate the noise robustness of DNN-based acoustic models and find that they can match state-of-the-art performance on the Aurora 4 task without any explicit noise compensation. This performance can be further improved by incorporating information about the environment into DNN training using a new method called noise-aware training. When combined with the recently proposed dropout training technique, a 7.5% relative improvement over the previously best published result on this task is achieved using only a single decoding pass and no additional decoding complexity compared to a standard DNN.