We present a new approach to perform zero-shot cross-modal transfer between speech and text for translation tasks. Multilingual speech and text are encoded in a joint fixed-size representation space. Then, we compare different approaches to decode these multimodal and multilingual fixed-size representations, enabling zero-shot translation between languages and modalities. All our models are trained without the need of cross-modal labeled translation data. Despite a fixed-size representation, we achieve very competitive results on several text and speech translation tasks. In particular, we significantly improve the state-of-the-art for zero-shot speech translation on Must-C. Incorporating a speech decoder in our framework, we introduce the first results for zero-shot direct speech-to-speech and text-to-speech translation.

该论文提出了一种新的零样本跨模态翻译方法，通过将多语言语音和文本编码到联合的固定大小的表示空间中，然后比较不同的解码方法以实现跨语言和模态的零样本翻译。尽管有着固定大小的表示空间，但在多个文本和语音翻译任务中取得了非常好的结果，特别是在Must-C上零样本语音翻译部分显著改善了最新技术的表现。在该方法的框架中，引入了一个语音解码器，实现了零样本直接语音到语音和文本到语音的翻译。

T-Modules: 零样本跨模态机器翻译的翻译模块