Over the past decades, a particular focus is given to research on machine learning
techniques for speech processing applications. However, in the past few years, research …

Generative adversarial networks (GANs) have seen remarkable progress in recent years.
They are used as generative models for all kinds of data such as text, images, audio, music …

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 …

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 …

The success of time-frequency (TF) mask-based approaches is dependent on the accuracy
of predicted mask given the noisy spectral features. The state-of-the-art methods in TF …

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 …

Abstract Accurate estimation of Room Impulse Response (RIR) which captures an
environment's acoustic properties is important for speech processing and AR/VR …

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 …