Learning in your voice: Non-parallel voice conversion based on speaker consistency loss
arXiv:2011.02168
Abstract
In this paper, we propose a novel voice conversion strategy to resolve the mismatch between the training and conversion scenarios when parallel speech corpus is unavailable for training. Based on auto-encoder and disentanglement frameworks, we design the proposed model to extract identity and content representations while reconstructing the input speech signal itself. Since we use other speaker's identity information in the training process, the training philosophy is naturally matched with the objective of voice conversion process. In addition, we effectively design the disentanglement framework to reliably preserve linguistic information and to enhance the quality of converted speech signals. The superiority of the proposed method is shown in subjective listening tests as well as objective measures.
arXiv commentICASSP 2021 submitted
References in corpus (5)
- Voice Conversion from Unaligned Corpora using Variational Autoencoding Wasserstein Generative Adversarial Networks
- ACVAE-VC: Non-parallel many-to-many voice conversion with auxiliary classifier variational autoencoder
- One-shot Voice Conversion by Separating Speaker and Content Representations with Instance Normalization
- StarGAN-VC2: Rethinking Conditional Methods for StarGAN-Based Voice Conversion
- VQVC+: One-Shot Voice Conversion by Vector Quantization and U-Net architecture