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

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

A method for statistical parametric speech synthesis incorporating generative adversarial
networks (GANs) is proposed. Although powerful deep neural networks techniques can be …

This paper investigates the use of Deep Bidirectional Long Short-Term Memory based
Recurrent Neural Networks (DBLSTM-RNNs) for voice conversion. Temporal correlations …

In this paper, a neural network named sequence-to-sequence ConvErsion NeTwork
(SCENT) is presented for acoustic modeling in voice conversion. At training stage, a SCENT …

An algorithm is proposed for estimating the band aperiodicity of speech signals, where
“aperiodicity” is defined as the power ratio between the speech signal and the aperiodic …

This paper describes the Voice Conversion Challenge 2016 devised by the authors to better
understand different voice conversion (VC) techniques by comparing their performance on a …

GIOL I5/04(2013.01) A method and system is disclosed for non-parametric speech GIOL
I5/4(2006.01) conversion. A text-to-speech (TTS) synthesis system may GIOL I3/02(2013.01) …