Speech-to-speech translation is a typical sequence-to-sequence learning task
that naturally has two directions. How to effectively leverage bidirectional
supervision signals to produce high-fidelity audio for both directions?
Existing approaches either train two separate models or a multitask-learned
model with low efficiency and inferior performance. In this paper, we propose a
duplex diffusion model that applies diffusion probabilistic models to both
sides of a reversible duplex Conformer, so that either end can simultaneously
input and output a distinct language's speech. Our model enables reversible
speech translation by simply flipping the input and output ends. Experiments
show that our model achieves the first success of reversible speech translation
with significant improvements of ASR-BLEU scores compared with a list of
state-of-the-art baselines.

本文提出了一种基于双向扩散模型的可逆性语音翻译方法，通过将扩散概率模型应用于可逆的 Conformer 的两侧，使两个方向都能同时输入和输出不同语言的语音。实验表明，该模型在 ASR-BLEU 得分方面取得了显著的改进，成为了可逆性语音翻译的第一项成功。