  Recently, recurrent neural network transducer (RNN-T) gains increasing
popularity due to its natural streaming capability as well as superior
performance. Nevertheless, RNN-T training requires large time and computation
resources as RNN-T loss calculation is slow and consumes a lot of memory.
Another limitation of RNN-T is that it tends to access more contexts for better
performance, thus leading to higher emission latency in streaming ASR. In this
paper we propose boundary-aware transducer (BAT) for memory-efficient and
low-latency ASR. In BAT, the lattice for RNN-T loss computation is reduced to a
restricted region selected by the alignment from continuous integrate-and-fire
(CIF), which is jointly optimized with the RNN-T model. Extensive experiments
demonstrate that compared to RNN-T, BAT reduces time and memory consumption
significantly in training, and achieves good CER-latency trade-offs in
inference for streaming ASR.
