  In this paper, we propose a textless acoustic model with a self-supervised
distillation strategy for noise-robust expressive speech-to-speech translation
(S2ST). Recently proposed expressive S2ST systems have achieved impressive
expressivity preservation performances by cascading unit-to-speech (U2S)
generator to the speech-to-unit translation model. However, these systems are
vulnerable to the presence of noise in input speech, which is an assumption in
real-world translation scenarios. To address this limitation, we propose a U2S
generator that incorporates a distillation with no label (DINO) self-supervised
training strategy into it's pretraining process. Because the proposed method
captures noise-agnostic expressivity representation, it can generate qualified
speech even in noisy environment. Objective and subjective evaluation results
verified that the proposed method significantly improved the performance of the
expressive S2ST system in noisy environments while maintaining competitive
performance in clean environments.
