  We propose a streaming non-autoregressive (non-AR) decoding algorithm to
deliberate the hypothesis alignment of a streaming RNN-T model. Our algorithm
facilitates a simple greedy decoding procedure, and at the same time is capable
of producing the decoding result at each frame with limited right context, thus
enjoying both high efficiency and low latency. These advantages are achieved by
converting the offline Align-Refine algorithm to be streaming-compatible, with
a novel transformer decoder architecture that performs local self-attentions
for both text and audio, and a time-aligned cross-attention at each layer.
Furthermore, we perform discriminative training of our model with the minimum
word error rate (MWER) criterion, which has not been done in the non-AR
decoding literature. Experiments on voice search datasets and Librispeech show
that with reasonable right context, our streaming model performs as well as the
offline counterpart, and discriminative training leads to further WER gain when
the first-pass model has small capacity.

Computation and Language;Machine Learning;Audio and Speech Processing