Enhancing expressivity transfer in textless speech-to-speech translation
Jarod Duret Benjamin O'Brien Yannick Estève Titouan Parcollet
Textless speech-to-speech translation systems are rapidly advancing, thanks
to the integration of self-supervised learning techniques. However, existing
state-of-the-art systems fall short when it comes to capturing and transferring
expressivity accurately across different languages. Expressivity plays a vital
role in conveying emotions, nuances, and cultural subtleties, thereby enhancing
communication across diverse languages. To address this issue this study
presents a novel method that operates at the discrete speech unit level and
leverages multilingual emotion embeddings to capture language-agnostic
information. Specifically, we demonstrate how these embeddings can be used to
effectively predict the pitch and duration of speech units in the target
language. Through objective and subjective experiments conducted on a
French-to-English translation task, our findings highlight the superior
expressivity transfer achieved by our approach compared to current
state-of-the-art systems.