One-Shot Voice Conversion by Vector Quantization

Da-Yi Wu; Hung-Yi Lee

ICASSP 2020accepted0 citations

One-Shot Voice Conversion by Vector Quantization

Abstract

In this paper, we propose a vector quantization (VQ) based one-shot voice conversion (VC) approach without any supervision on speaker label. We model the content embedding as a series of discrete codes and take the difference between quantize-before and quantize-after vector as the speaker embedding. We show that this approach has a strong ability to disentangle the content and speaker information with reconstruction loss only, and one-shot VC is thus achieved.

BibTeX

@inproceedings{icassp2020_oneshotvoiceconv,
  title = {One-Shot Voice Conversion by Vector Quantization},
  author = {Da-Yi Wu and Hung-Yi Lee},
  booktitle = {ICASSP 2020},
  year = {2020}
}