An overview of voice conversion and its challenges: From statistical modeling to deep learning
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 …
conversion, we change the speaker identity from one to another, while keeping the linguistic …
An overview of voice conversion systems
SH Mohammadi, A Kain - Speech Communication, 2017 - Elsevier
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 …
preserving linguistic information. A subset of VT, Voice conversion (VC) specifically aims to …
Cyclegan-vc: Non-parallel voice conversion using cycle-consistent adversarial networks
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 …
source to target speech without relying on parallel data. The proposed method is particularly …
Parallel-data-free voice conversion using cycle-consistent adversarial networks
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 …
from source to target speech without relying on parallel data. The proposed method is …
Statistical parametric speech synthesis incorporating generative adversarial networks
Y Saito, S Takamichi… - IEEE/ACM Transactions on …, 2017 - ieeexplore.ieee.org
A method for statistical parametric speech synthesis incorporating generative adversarial
networks (GANs) is proposed. Although powerful deep neural networks techniques can be …
networks (GANs) is proposed. Although powerful deep neural networks techniques can be …
Voice conversion using deep bidirectional long short-term memory based recurrent neural networks
This paper investigates the use of Deep Bidirectional Long Short-Term Memory based
Recurrent Neural Networks (DBLSTM-RNNs) for voice conversion. Temporal correlations …
Recurrent Neural Networks (DBLSTM-RNNs) for voice conversion. Temporal correlations …
Sequence-to-sequence acoustic modeling for voice conversion
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 …
(SCENT) is presented for acoustic modeling in voice conversion. At training stage, a SCENT …
[HTML][HTML] D4C, a band-aperiodicity estimator for high-quality speech synthesis
M Morise - Speech Communication, 2016 - Elsevier
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 …
“aperiodicity” is defined as the power ratio between the speech signal and the aperiodic …
[PDF][PDF] The Voice Conversion Challenge 2016.
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 …
understand different voice conversion (VC) techniques by comparing their performance on a …
Method and system for non-parametric voice conversion
I Agiomyrgiannakis - US Patent 9,183,830, 2015 - Google Patents
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) …
I5/4(2006.01) conversion. A text-to-speech (TTS) synthesis system may GIOL I3/02(2013.01) …