Attention-based end-to-end models such as Listen, Attend and Spell (LAS),
simplify the whole pipeline of traditional automatic speech recognition (ASR)
systems and become popular in the field of speech recognition. In previous
work, researchers have shown that such architectures can acquire comparable
results to state-of-the-art ASR systems, especially when using a bidirectional
encoder and global soft attention (GSA) mechanism. However, bidirectional
encoder and GSA are two obstacles for real-time speech recognition. In this
work, we aim to stream LAS baseline by removing the above two obstacles. On the
encoder side, we use a latency-controlled (LC) bidirectional structure to
reduce the delay of forward computation. Meanwhile, an adaptive monotonic
chunk-wise attention (AMoChA) mechanism is proposed to replace GSA for the
calculation of attention weight distribution. Furthermore, we propose two
methods to alleviate the huge performance degradation when combining LC and
AMoChA. Finally, we successfully acquire an online LAS model, LC-AMoChA, which
has only 3.5% relative performance reduction to LAS baseline on our internal
Mandarin corpus.

本文旨在通过采用延迟控制的双向结构和自适应单调分块注意力机制来消除全局软注意机制和双向编码器对实时语音识别的阻碍，并提出了两种方法来缓解两种方法结合时的性能下降，从而成功地获得了一个在线的 LAS 模型 LC-AMoChA。