ASAPP ASR: Multistream CNN and Self Attentive SRU for SOTA Speech Recognition
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.
INTERSPEECH