An investigation of deep neural networks for noise robust speech recognition
Top Cited Papers
- 1 May 2013
- conference paper
- conference paper
- Published by Institute of Electrical and Electronics Engineers (IEEE)
- No. 15206149,p. 7398-7402
- https://doi.org/10.1109/icassp.2013.6639100
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.Keywords
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