'Voice Conversion' paper candidate 2411.02402

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article info.

• title: Optimal Transport Maps are Good Voice Converters

• summary: Recently, neural network-based methods for computing optimal transport maps
  have been effectively applied to style transfer problems. However, the
  application of these methods to voice conversion is underexplored. In our
  paper, we fill this gap by investigating optimal transport as a framework for
  voice conversion. We present a variety of optimal transport algorithms designed
  for different data representations, such as mel-spectrograms and latent
  representation of self-supervised speech models. For the mel-spectogram data
  representation, we achieve strong results in terms of Frechet Audio Distance
  (FAD). This performance is consistent with our theoretical analysis, which
  suggests that our method provides an upper bound on the FAD between the target
  and generated distributions. Within the latent space of the WavLM encoder, we
  achived state-of-the-art results and outperformed existing methods even with
  limited reference speaker data.

• id: http://arxiv.org/abs/2411.02402v1

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