Deepfakes as a threat to a speaker and facial recognition: An overview of tools and attack vectors
Deepfakes present an emerging threat in cyberspace. Recent developments in machine
learning make deepfakes highly believable, and very difficult to differentiate between what is …
learning make deepfakes highly believable, and very difficult to differentiate between what is …
Contentvec: An improved self-supervised speech representation by disentangling speakers
Self-supervised learning in speech involves training a speech representation network on a
large-scale unannotated speech corpus, and then applying the learned representations to …
large-scale unannotated speech corpus, and then applying the learned representations to …
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 …
Unsupervised speech decomposition via triple information bottleneck
Speech information can be roughly decomposed into four components: language content,
timbre, pitch, and rhythm. Obtaining disentangled representations of these components is …
timbre, pitch, and rhythm. Obtaining disentangled representations of these components is …
Again-vc: A one-shot voice conversion using activation guidance and adaptive instance normalization
Recently, voice conversion (VC) has been widely studied. Many VC systems use
disentangle-based learning techniques to separate the speaker and the linguistic content …
disentangle-based learning techniques to separate the speaker and the linguistic content …
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 …
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 …
Towards relatable explainable AI with the perceptual process
Machine learning models need to provide contrastive explanations, since people often seek
to understand why a puzzling prediction occurred instead of some expected outcome …
to understand why a puzzling prediction occurred instead of some expected outcome …
Avqvc: One-shot voice conversion by vector quantization with applying contrastive learning
Voice Conversion (VC) refers to changing the timbre of a speech while retaining the
discourse content. Recently, many works have focused on disentangle-based learning …
discourse content. Recently, many works have focused on disentangle-based learning …
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 …