One-shot Voice Conversion by Separating Speaker and Content Representations with Instance Normalization

TL;DR

This paper introduces a one-shot voice conversion method that disentangles speaker and content representations using instance normalization, enabling conversion with only one example utterance per speaker, even if unseen during training.

Contribution

The proposed approach allows one-shot voice conversion by separating speaker and content features without requiring parallel data or prior exposure to target speakers.

Findings

Achieves high similarity to target speaker voices in both objective and subjective tests.

Learns meaningful speaker representations without supervision.

Operates effectively on unseen speakers with only one example utterance.

Abstract

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.