Deepfakes generation and detection: State-of-the-art, open challenges, countermeasures, and way forward
Easy access to audio-visual content on social media, combined with the availability of
modern tools such as Tensorflow or Keras, and open-source trained models, along with …
modern tools such as Tensorflow or Keras, and open-source trained models, along with …
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 …
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 …
Vqmivc: Vector quantization and mutual information-based unsupervised speech representation disentanglement for one-shot voice conversion
One-shot voice conversion (VC), which performs conversion across arbitrary speakers with
only a single target-speaker utterance for reference, can be effectively achieved by speech …
only a single target-speaker utterance for reference, can be effectively achieved by speech …
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 …
Vqvc+: One-shot voice conversion by vector quantization and u-net architecture
Voice conversion (VC) is a task that transforms the source speaker's timbre, accent, and
tones in audio into another one's while preserving the linguistic content. It is still a …
tones in audio into another one's while preserving the linguistic content. It is still a …
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 …
Diffusion-based voice conversion with fast maximum likelihood sampling scheme
Voice conversion is a common speech synthesis task which can be solved in different ways
depending on a particular real-world scenario. The most challenging one often referred to as …
depending on a particular real-world scenario. The most challenging one often referred to as …
Emotion intensity and its control for emotional voice conversion
Emotional voice conversion (EVC) seeks to convert the emotional state of an utterance while
preserving the linguistic content and speaker identity. In EVC, emotions are usually treated …
preserving the linguistic content and speaker identity. In EVC, emotions are usually treated …
Global prosody style transfer without text transcriptions
Prosody plays an important role in characterizing the style of a speaker or an emotion, but
most non-parallel voice or emotion style transfer algorithms do not convert any prosody …
most non-parallel voice or emotion style transfer algorithms do not convert any prosody …