Speaker identity is one of the important characteristics of human speech. In voice
conversion, we change the speaker identity from one to another, while keeping the linguistic …

Voice transformation (VT) aims to change one or more aspects of a speech signal while
preserving linguistic information. A subset of VT, Voice conversion (VC) specifically aims to …

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 …

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 …

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

Non-parallel multi-domain voice conversion (VC) is a technique for learning mappings
among multiple domains without relying on parallel data. This is important but challenging …

In this paper, we describe a novel spectral conversion method for voice conversion (VC). A
Gaussian mixture model (GMM) of the joint probability density of source and target features …

An ability to model a generative process and learn a latent representation for speech in an
unsupervised fashion will be crucial to process vast quantities of unlabelled speech data …

In this paper, we use artificial neural networks (ANNs) for voice conversion and exploit the
mapping abilities of an ANN model to perform mapping of spectral features of a source …

This paper describes a method based on a sequence-to-sequence learning (Seq2Seq) with
attention and context preservation mechanism for voice conversion (VC) tasks. Seq2Seq …