The requirements for many applications of state-of-the-art speech recognition
systems include not only low word error rate (WER) but also low latency.
Specifically, for many use-cases, the system must be able to decode utterances
in a streaming fashion and faster than real-time. Recently, a streaming
recurrent neural network transducer (RNN-T) end-to-end (E2E) model has shown to
be a good candidate for on-device speech recognition, with improved WER and
latency metrics compared to conventional on-device models [1]. However, this
model still lags behind a large state-of-the-art conventional model in quality
[2]. On the other hand, a non-streaming E2E Listen, Attend and Spell (LAS)
model has shown comparable quality to large conventional models [3]. This work
aims to bring the quality of an E2E streaming model closer to that of a
conventional system by incorporating a LAS network as a second-pass component,
while still abiding by latency constraints. Our proposed two-pass model
achieves a 17%-22% relative reduction in WER compared to RNN-T alone and
increases latency by a small fraction over RNN-T.

本研究旨在加入 Listen, Attend and Spell 模型作为第二步流程，从而将端到端流式模型的性能提高至与传统语音识别系统相当，同时也满足计算和响应延迟等约束条件。