Recently, voice conversion (VC) without parallel data has been successfully
adapted to multi-target scenario in which a single model is trained to convert
the input voice to many different speakers. However, such model suffers from
the limitation that it can only convert the voice to the speakers in the
training data, which narrows down the applicable scenario of VC. In this paper,
we proposed a novel one-shot VC approach which is able to perform VC by only an
example utterance from source and target speaker respectively, and the source
and target speaker do not even need to be seen during training. This is
achieved by disentangling speaker and content representations with instance
normalization (IN). Objective and subjective evaluation shows that our model is
able to generate the voice similar to target speaker. In addition to the
performance measurement, we also demonstrate that this model is able to learn
meaningful speaker representations without any supervision.

本文提出了一种使用仅一次源和目标说话者示例语音，通过实例归一化来分解说话者和语音内容表示的一次性语音转换方法，无需事先见过源和目标说话者训练模型即可执行声音转换，目标语音及说话者的相似性得到了客观和主观的评估，同时表明该模型可以在没有任何监督的情况下学习含义深层次的说话者表示。