Voice Conversion from Unaligned Corpora using Variational Autoencoding Wasserstein Generative Adversarial Networks

04/04/2017
by   Chin-Cheng Hsu, et al.
0

Building a voice conversion (VC) system from non-parallel speech corpora is challenging but highly valuable in real application scenarios. In most situations, the source and the target speakers do not repeat the same texts or they may even speak different languages. In this case, one possible, although indirect, solution is to build a generative model for speech. Generative models focus on explaining the observations with latent variables instead of learning a pairwise transformation function, thereby bypassing the requirement of speech frame alignment. In this paper, we propose a non-parallel VC framework with a variational autoencoding Wasserstein generative adversarial network (VAW-GAN) that explicitly considers a VC objective when building the speech model. Experimental results corroborate the capability of our framework for building a VC system from unaligned data, and demonstrate improved conversion quality.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
11/02/2020

CVC: Contrastive Learning for Non-parallel Voice Conversion

Cycle consistent generative adversarial network (CycleGAN) and variation...
research
07/21/2021

StarGANv2-VC: A Diverse, Unsupervised, Non-parallel Framework for Natural-Sounding Voice Conversion

We present an unsupervised non-parallel many-to-many voice conversion (V...
research
04/25/2021

An Adaptive Learning based Generative Adversarial Network for One-To-One Voice Conversion

Voice Conversion (VC) emerged as a significant domain of research in the...
research
06/06/2018

StarGAN-VC: Non-parallel many-to-many voice conversion with star generative adversarial networks

This paper proposes a method that allows for non-parallel many-to-many v...
research
08/27/2020

Non-Parallel Voice Conversion with Augmented Classifier Star Generative Adversarial Networks

We have previously proposed a method that allows for non-parallel voice ...
research
12/04/2022

Generative Models for Improved Naturalness, Intelligibility, and Voicing of Whispered Speech

This work adapts two recent architectures of generative models and evalu...
research
11/07/2019

Change your singer: a transfer learning generative adversarial framework for song to song conversion

Have you ever wondered how a song might sound if performed by a differen...

Please sign up or login with your details

Forgot password? Click here to reset