Recently, cycle-consistent adversarial network (Cycle-GAN) has been successfully applied
to voice conversion to a different speaker without parallel data, although in those …

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 …

This paper proposes a method that allows non-parallel many-to-many voice conversion (VC)
by using a variant of a generative adversarial network (GAN) called StarGAN. Our method …

One-shot voice conversion (VC) with only a single target speaker's speech for reference has
become a hot research topic. Existing works generally disentangle timbre, while information …

We propose a training framework for sequence-to-sequence voice conversion (SVC). A well-
known problem regarding a conventional VC framework is that acoustic-feature sequences …

Any-to-any voice conversion (VC) aims to convert the timbre of utterances from and to any
speakers seen or unseen during training. Various any-to-any VC approaches have been …

Non-parallel voice conversion (VC) is a technique for learning the mapping from source to
target speech without relying on parallel data. This is an important task, but it has been …

Although voice conversion (VC) algorithms have achieved remarkable success along with
the development of machine learning, superior performance is still difficult to achieve when …

This article proposes a voice conversion (VC) method using sequence-to-sequence
(seq2seq or S2S) learning, which flexibly converts not only the voice characteristics but also …

We propose a non-parallel voice-conversion (VC) method that can learn a mapping from
source to target speech without relying on parallel data. The proposed method is particularly …