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A review on generative adversarial networks: Algorithms, theory, and applications
Generative adversarial networks (GANs) have recently become a hot research topic;
however, they have been studied since 2014, and a large number of algorithms have been …
however, they have been studied since 2014, and a large number of algorithms have been …
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 kee** the linguistic …
conversion, we change the speaker identity from one to another, while kee** the linguistic …
Autovc: Zero-shot voice style transfer with only autoencoder loss
Despite the progress in voice conversion, many-to-many voice conversion trained on non-
parallel data, as well as zero-shot voice conversion, remains under-explored. Deep style …
parallel data, as well as zero-shot voice conversion, remains under-explored. Deep style …
Emotional voice conversion: Theory, databases and esd
In this paper, we first provide a review of the state-of-the-art emotional voice conversion
research, and the existing emotional speech databases. We then motivate the development …
research, and the existing emotional speech databases. We then motivate the development …
[HTML][HTML] A review of synthetic image data and its use in computer vision
Development of computer vision algorithms using convolutional neural networks and deep
learning has necessitated ever greater amounts of annotated and labelled data to produce …
learning has necessitated ever greater amounts of annotated and labelled data to produce …
Vqmivc: Vector quantization and mutual information-based unsupervised speech representation disentanglement for one-shot voice conversion
Cyclegan-vc: Non-parallel voice conversion using cycle-consistent adversarial networks
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 …
source to target speech without relying on parallel data. The proposed method is particularly …
One-shot voice conversion by separating speaker and content representations with instance normalization
Recently, voice conversion (VC) without parallel data has been successfully adapted to multi-
target scenario in which a single model is trained to convert the input voice to many different …
target scenario in which a single model is trained to convert the input voice to many different …