Simultaneous speech-to-text translation is widely useful in many scenarios.
The conventional cascaded approach uses a pipeline of streaming ASR followed by
simultaneous MT, but suffers from error propagation and extra latency. To
alleviate these issues, recent efforts attempt to directly translate the source
speech into target text simultaneously, but this is much harder due to the
combination of two separate tasks. We instead propose a new paradigm with the
advantages of both cascaded and end-to-end approaches. The key idea is to use
two separate, but synchronized, decoders on streaming ASR and direct
speech-to-text translation (ST), respectively, and the intermediate results of
ASR guide the decoding policy of (but is not fed as input to) ST. During
training time, we use multitask learning to jointly learn these two tasks with
a shared encoder. En-to-De and En-to-Es experiments on the MuSTC dataset
demonstrate that our proposed technique achieves substantially better
translation quality at similar levels of latency.

本文提出一种新型的语音翻译范例，通过使用两个分离但同步的解码器，一个用于流式 ASR, 一个用于直接语音翻译，并通过 ASR 生成的中间结果指导直接语音翻译，实现了流畅度更高的翻译质量。

利用同步的流式 ASR 辅助的直接同声传译

Direct Simultaneous Speech-to-Text Translation Assisted by Synchronized  Streaming ASR

We present a simple approach to improve direct speech-to-text translation
(ST) when the source language is low-resource: we pre-train the model on a
high-resource automatic speech recognition (ASR) task, and then fine-tune its
parameters for ST. We demonstrate that our approach is effective by
pre-training on 300 hours of English ASR data to improve Spanish-English ST
from 10.8 to 20.2 BLEU when only 20 hours of Spanish-English ST training data
are available. Through an ablation study, we find that the pre-trained encoder
(acoustic model) accounts for most of the improvement, despite the fact that
the shared language in these tasks is the target language text, not the source
language audio. Applying this insight, we show that pre-training on ASR helps
ST even when the ASR language differs from both source and target ST languages:
pre-training on French ASR also improves Spanish-English ST. Finally, we show
that the approach improves performance on a true low-resource task:
pre-training on a combination of English ASR and French ASR improves
Mboshi-French ST, where only 4 hours of data are available, from 3.5 to 7.1
BLEU.

本文研究通过使用大量训练数据的自动语音识别任务，对语音翻译进行预训练，以提高低资源环境下的语音翻译性能，其中预训练的声学模型起到了关键的作用，并且可用于不同语言对之间的翻译。经验证本方法效果显著，能够在真实的低资源任务中提高性能。