In this paper, we propose a simple yet effective framework for multilingual
end-to-end speech translation (ST), in which speech utterances in source
languages are directly translated to the desired target languages with a
universal sequence-to-sequence architecture. While multilingual models have
shown to be useful for automatic speech recognition (ASR) and machine
translation (MT), this is the first time they are applied to the end-to-end ST
problem. We show the effectiveness of multilingual end-to-end ST in two
scenarios: one-to-many and many-to-many translations with publicly available
data. We experimentally confirm that multilingual end-to-end ST models
significantly outperform bilingual ones in both scenarios. The generalization
of multilingual training is also evaluated in a transfer learning scenario to a
very low-resource language pair. All of our codes and the database are publicly
available to encourage further research in this emergent multilingual ST topic.

本文提出了一种简单且有效的多语言端到端语音翻译框架，并证明了其在自动语音识别、机器翻译、一对多翻译以及多对多翻译中的有效性以及相对于双语端到端语音翻译的优势。