  In this paper we present state-of-the-art (SOTA) performance on the
LibriSpeech corpus with two novel neural network architectures, a multistream
CNN for acoustic modeling and a self-attentive simple recurrent unit (SRU) for
language modeling. In the hybrid ASR framework, the multistream CNN acoustic
model processes an input of speech frames in multiple parallel pipelines where
each stream has a unique dilation rate for diversity. Trained with the
SpecAugment data augmentation method, it achieves relative word error rate
(WER) improvements of 4% on test-clean and 14% on test-other. We further
improve the performance via N-best rescoring using a 24-layer self-attentive
SRU language model, achieving WERs of 1.75% on test-clean and 4.46% on
test-other.
