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Direct speech-to-speech translation (S2ST) has gradually become popular as ithas many advantages compared with cascade S2ST. However, current researchmainly focuses on the accuracy of semantic translation and ignores the speechstyle transfer from a source language to a target language. The lack ofhigh-fidelity expressive parallel data makes such style transfer challenging,especially in more practical zero-shot scenarios. To solve this problem, wefirst build a parallel corpus using a multi-lingual multi-speakertext-to-speech synthesis (TTS) system and then propose the StyleS2ST model withcross-lingual speech style transfer ability based on a style adaptor on adirect S2ST system framework. Enabling continuous style space modeling of anacoustic model through parallel corpus training and non-parallel TTS dataaugmentation, StyleS2ST captures cross-lingual acoustic feature mapping fromthe source to the target language. Experiments show that StyleS2ST achievesgood style similarity and naturalness in both in-set and out-of-set zero-shotscenarios.

StyleS2ST: Zero-shot Style Transfer for Direct Speech-to-speech Translation