  In this work, we exploit speech enhancement for improving a recurrent neural
network transducer (RNN-T) based ASR system. We employ a dense convolutional
recurrent network (DCRN) for complex spectral mapping based speech enhancement,
and find it helpful for ASR in two ways: a data augmentation technique, and a
preprocessing frontend. In using it for ASR data augmentation, we exploit a KL
divergence based consistency loss that is computed between the ASR outputs of
original and enhanced utterances. In using speech enhancement as an effective
ASR frontend, we propose a three-step training scheme based on model
pretraining and feature selection. We evaluate our proposed techniques on a
challenging social media English video dataset, and achieve an average relative
improvement of 11.2% with speech enhancement based data augmentation, 8.3% with
enhancement based preprocessing, and 13.4% when combining both.
