'Voice Conversion' paper candidate 2501.04416

[github-actions]

Please check whether this paper is about 'Voice Conversion' or not.

article info.

• title: ZSVC: Zero-shot Style Voice Conversion with Disentangled Latent Diffusion Models and Adversarial Training

• summary: Style voice conversion aims to transform the speaking style of source speech
  into a desired style while keeping the original speaker's identity. However,
  previous style voice conversion approaches primarily focus on well-defined
  domains such as emotional aspects, limiting their practical applications. In
  this study, we present ZSVC, a novel Zero-shot Style Voice Conversion approach
  that utilizes a speech codec and a latent diffusion model with speech prompting
  mechanism to facilitate in-context learning for speaking style conversion. To
  disentangle speaking style and speaker timbre, we introduce information
  bottleneck to filter speaking style in the source speech and employ Uncertainty
  Modeling Adaptive Instance Normalization (UMAdaIN) to perturb the speaker
  timbre in the style prompt. Moreover, we propose a novel adversarial training
  strategy to enhance in-context learning and improve style similarity.
  Experiments conducted on 44,000 hours of speech data demonstrate the superior
  performance of ZSVC in generating speech with diverse speaking styles in
  zero-shot scenarios.

• id: http://arxiv.org/abs/2501.04416v1

judge

Write [vclab::confirmed] or [vclab::excluded] in comment.