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。

TransVIP: 保留声音和等时性的语音到语音翻译系统

TransVIP: Speech to Speech Translation System with Voice and Isochrony  Preservation

Video dubbing aims to translate the original speech in a film or television
program into the speech in a target language, which can be achieved with a
cascaded system consisting of speech recognition, machine translation and
speech synthesis. To ensure the translated speech to be well aligned with the
corresponding video, the length/duration of the translated speech should be as
close as possible to that of the original speech, which requires strict length
control. Previous works usually control the number of words or characters
generated by the machine translation model to be similar to the source
sentence, without considering the isochronicity of speech as the speech
duration of words/characters in different languages varies. In this paper, we
propose a machine translation system tailored for the task of video dubbing,
which directly considers the speech duration of each token in translation, to
match the length of source and target speech. Specifically, we control the
speech length of generated sentence by guiding the prediction of each word with
the duration information, including the speech duration of itself as well as
how much duration is left for the remaining words. We design experiments on
four language directions (German -> English, Spanish -> English, Chinese <->
English), and the results show that the proposed method achieves better length
control ability on the generated speech than baseline methods. To make up the
lack of real-world datasets, we also construct a real-world test set collected
from films to provide comprehensive evaluations on the video dubbing task.

该研究针对影视配音中原始语音和目标语音的对齐问题，提出一种机器翻译系统，通过考虑语音时长控制翻译长度，构建了真实世界情景下的测试集，实现了对自然长度的更好控制。