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 …

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 …

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 …

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 …

Speech information can be roughly decomposed into four components: language content,
timbre, pitch, and rhythm. Obtaining disentangled representations of these components is …

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 …

Recently, voice conversion (VC) has been widely studied. Many VC systems use
disentangle-based learning techniques to separate the speaker and the linguistic content …

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 …

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 …

Diffusion-based generative models have recently exhibited powerful generative
performance. However, as many attributes exist in the data distribution and owing to several …