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 …

This paper presents a new spectral envelope conversion method using deep neural
networks (DNNs). The conventional joint density Gaussian mixture model (JDGMM) based …

In this study, we trained a deep autoencoder to build compact representations of short-term
spectra of multiple speakers. Using this compact representation as mapping features, we …

The generated speech of hidden Markov model (HMM)-based statistical parametric speech
synthesis still sounds “muffled.” One cause of this degradation in speech quality may be the …

In the recent past years, deep learning based machine learning systems have demonstrated
remarkable success for a wide range of learning tasks in multiple domains such as computer …

In this paper we propose a deep neural network to model the conditional probability of the
spectral differences between natural and synthetic speech. This allows us to reconstruct the …

Speech is a primary mode of communication between human beings. With that, the need of
creating artificial speech became a dream of humankind from the beginning of the 1980s. As …

This paper presents the HCCL-CUHK system for the Voice Conversion Challenge 2018 (the
VCC 2018), which is mainly characterized by doing Voice Conversion (VC) with nonparallel …

Recently, researchers have begun to investigate Deep Neural Network (DNN) architectures
as mapping functions in voice conversion systems. In this study, we propose a novel …

This paper presents a deep neural network (DNN) based spectral envelope conversion
method. A global DNN is employed to model the complex non-linear mapping relationship …