There is a rising interest and trend in research towards directly translating
speech from one language to another, known as end-to-end speech-to-speech
translation. However, most end-to-end models struggle to outperform cascade
models, i.e., a pipeline framework by concatenating speech recognition, machine
translation and text-to-speech models. The primary challenges stem from the
inherent complexities involved in direct translation tasks and the scarcity of
data. In this study, we introduce a novel model framework TransVIP that
leverages diverse datasets in a cascade fashion yet facilitates end-to-end
inference through joint probability. Furthermore, we propose two separated
encoders to preserve the speaker's voice characteristics and isochrony from the
source speech during the translation process, making it highly suitable for
scenarios such as video dubbing. Our experiments on the French-English language
pair demonstrate that our model outperforms the current state-of-the-art
speech-to-speech translation model.

该研究介绍了一种名为 TransVIP 的新模型框架，该模型通过级联方式利用多样的数据集，在维持发言者的声音特征和等时性的同时实现端到端推理，适用于视频配音等场景，并在法英语言对上的实验中表现出优于当前最先进的语音到语音翻译模型的性能。