Voice style transfer, also called voice conversion, seeks to modify one
speaker's voice to generate speech as if it came from another (target) speaker.
Previous works have made progress on voice conversion with parallel training
data and pre-known speakers. However, zero-shot voice style transfer, which
learns from non-parallel data and generates voices for previously unseen
speakers, remains a challenging problem. We propose a novel zero-shot voice
transfer method via disentangled representation learning. The proposed method
first encodes speaker-related style and voice content of each input voice into
separated low-dimensional embedding spaces, and then transfers to a new voice
by combining the source content embedding and target style embedding through a
decoder. With information-theoretic guidance, the style and content embedding
spaces are representative and (ideally) independent of each other. On
real-world VCTK datasets, our method outperforms other baselines and obtains
state-of-the-art results in terms of transfer accuracy and voice naturalness
for voice style transfer experiments under both many-to-many and zero-shot
setups.

本研究提出了一种基于解缠表示学习的零样本语音转换方法，通过在输入语音的内容嵌入和目标样式嵌入之间进行组合来实现对新声音的转换，可以在无先验知识和非并行数据的情况下完成，方法在 VCTK 数据集上进行验证，并在许多对多和零样本实验上展现出了最新的标志性结果。