End-to-end (E2E) models fold the acoustic, pronunciation and language models
of a conventional speech recognition model into one neural network with a much
smaller number of parameters than a conventional ASR system, thus making it
suitable for on-device applications. For example, recurrent neural network
transducer (RNN-T) as a streaming E2E model has shown promising potential for
on-device ASR. For such applications, quality and latency are two critical
factors. We propose to reduce E2E model's latency by extending the RNN-T
endpointer (RNN-T EP) model with additional early and late penalties. By
further applying the minimum word error rate (MWER) training technique, we
achieved 8.0% relative word error rate (WER) reduction and 130ms 90-percentile
latency reduction over on a Voice Search test set. We also experimented with a
second-pass Listen, Attend and Spell (LAS) rescorer . Although it did not
directly improve the first pass latency, the large WER reduction provides extra
room to trade WER for latency. RNN-T EP+LAS, together with MWER training brings
in 18.7% relative WER reduction and 160ms 90-percentile latency reductions
compared to the original proposed RNN-T EP model.

本文提出了使用延迟惩罚技巧和最小词错误率训练技术来减少端到端模型延迟以提高其质量的方法，并在语音识别方面展示出重要改进。