Speaker identity is one of the important characteristics of human speech. In voice
conversion, we change the speaker identity from one to another, while keeping the linguistic …

In the last few years, with the advancement of deep learning methods, especially Generative
Adversarial Networks (GANs) and Variational Auto-encoders (VAEs), fabricated content has …

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 …

Voice conversion (VC) can be achieved by first extracting source content information and
target speaker information, and then reconstructing waveform with these information …

Emotional speech synthesis aims to synthesize human voices with various emotional effects.
The current studies are mostly focused on imitating an averaged style belonging to a specific …

Traditional studies on voice conversion (VC) have made progress with parallel training data
and known speakers. Good voice conversion quality is obtained by exploring better …

One-shot voice conversion (VC) aims to convert speech from any source speaker to an
arbitrary target speaker with only a few seconds of reference speech from the target speaker …

Direct speech-to-speech translation (S2ST) aims to convert speech from one language into
another, and has demonstrated significant progress to date. Despite the recent success …

Disentangling content and speaking style information is essential for zero-shot non-parallel
voice conversion (VC). Our previous study investigated a novel framework with disentangled …

This paper proposes RefXVC, a method for cross-lingual voice conversion (XVC) that
leverages reference information to improve conversion performance. Previous XVC works …