'Voice Conversion' paper candidate 2412.20359

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• title: EmoReg: Directional Latent Vector Modeling for Emotional Intensity Regularization in Diffusion-based Voice Conversion

• summary: The Emotional Voice Conversion (EVC) aims to convert the discrete emotional
  state from the source emotion to the target for a given speech utterance while
  preserving linguistic content. In this paper, we propose regularizing emotion
  intensity in the diffusion-based EVC framework to generate precise speech of
  the target emotion. Traditional approaches control the intensity of an
  emotional state in the utterance via emotion class probabilities or intensity
  labels that often lead to inept style manipulations and degradations in
  quality. On the contrary, we aim to regulate emotion intensity using
  self-supervised learning-based feature representations and unsupervised
  directional latent vector modeling (DVM) in the emotional embedding space
  within a diffusion-based framework. These emotion embeddings can be modified
  based on the given target emotion intensity and the corresponding direction
  vector. Furthermore, the updated embeddings can be fused in the reverse
  diffusion process to generate the speech with the desired emotion and
  intensity. In summary, this paper aims to achieve high-quality emotional
  intensity regularization in the diffusion-based EVC framework, which is the
  first of its kind work. The effectiveness of the proposed method has been shown
  across state-of-the-art (SOTA) baselines in terms of subjective and objective
  evaluations for the English and Hindi languages \footnote{Demo samples are
  available at the following URL: \url{https://nirmesh-sony.github.io/EmoReg/}}.

• id: http://arxiv.org/abs/2412.20359v1

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