Recently, Transformer has gained success in automatic speech recognition
(ASR) field. However, it is challenging to deploy a Transformer-based
end-to-end (E2E) model for online speech recognition. In this paper, we propose
the Transformer-based online CTC/attention E2E ASR architecture, which contains
the chunk self-attention encoder (chunk-SAE) and the monotonic truncated
attention (MTA) based self-attention decoder (SAD). Firstly, the chunk-SAE
splits the speech into isolated chunks. To reduce the computational cost and
improve the performance, we propose the state reuse chunk-SAE. Sencondly, the
MTA based SAD truncates the speech features monotonically and performs
attention on the truncated features. To support the online recognition, we
integrate the state reuse chunk-SAE and the MTA based SAD into online
CTC/attention architecture. We evaluate the proposed online models on the HKUST
Mandarin ASR benchmark and achieve a 23.66% character error rate (CER) with a
320 ms latency. Our online model yields as little as 0.19% absolute CER
degradation compared with the offline baseline, and achieves significant
improvement over our prior work on Long Short-Term Memory (LSTM) based online
E2E models.

本论文介绍了基于 Transformer 的在线 CTC/Attention E2E ASR 架构，该架构包括块自注意力编码器和基于单调截断注意力的自注意力解码器，通过将块自注意力编码器和基于单调截断注意力的自注意力解码器集成到在线 CTC/Attention 架构中，实现了在线语音识别，与离线基线相比，具有最低为 0.19％的 CER 衰减和显着的性能提升。