Cyclegan-vc2: Improved cyclegan-based non-parallel voice conversion
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 …
target speech without relying on parallel data. This is an important task, but it has been …
Cyclegan-vc3: Examining and improving cyclegan-vcs for mel-spectrogram conversion
Non-parallel voice conversion (VC) is a technique for learning mappings between source
and target speeches without using a parallel corpus. Recently, cycle-consistent adversarial …
and target speeches without using a parallel corpus. Recently, cycle-consistent adversarial …
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 …
Maskcyclegan-vc: Learning non-parallel voice conversion with filling in frames
Non-parallel voice conversion (VC) is a technique for training voice converters without a
parallel corpus. Cycle-consistent adversarial network-based VCs (CycleGAN-VC and …
parallel corpus. Cycle-consistent adversarial network-based VCs (CycleGAN-VC and …
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 …
Stargan-vc2: Rethinking conditional methods for stargan-based voice conversion
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 …
among multiple domains without relying on parallel data. This is important but challenging …
Starganv2-vc: A diverse, unsupervised, non-parallel framework for natural-sounding voice conversion
We present an unsupervised non-parallel many-to-many voice conversion (VC) method
using a generative adversarial network (GAN) called StarGAN v2. Using a combination of …
using a generative adversarial network (GAN) called StarGAN v2. Using a combination of …
Nvc-net: End-to-end adversarial voice conversion
B Nguyen, F Cardinaux - ICASSP 2022-2022 IEEE …, 2022 - ieeexplore.ieee.org
Voice conversion (VC) has gained increasing popularity in many speech synthesis
applications. The idea is to change the voice identity from one speaker into another while …
applications. The idea is to change the voice identity from one speaker into another while …
Multi-target voice conversion without parallel data by adversarially learning disentangled audio representations
Recently, cycle-consistent adversarial network (Cycle-GAN) has been successfully applied
to voice conversion to a different speaker without parallel data, although in those …
to voice conversion to a different speaker without parallel data, although in those …
High-quality nonparallel voice conversion based on cycle-consistent adversarial network
Although voice conversion (VC) algorithms have achieved remarkable success along with
the development of machine learning, superior performance is still difficult to achieve when …
the development of machine learning, superior performance is still difficult to achieve when …