'Voice Conversion' paper candidate 2409.17364

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• title: Exploring synthetic data for cross-speaker style transfer in style representation based TTS

• summary: Incorporating cross-speaker style transfer in text-to-speech (TTS) models is
  challenging due to the need to disentangle speaker and style information in
  audio. In low-resource expressive data scenarios, voice conversion (VC) can
  generate expressive speech for target speakers, which can then be used to train
  the TTS model. However, the quality and style transfer ability of the VC model
  are crucial for the overall TTS model quality. In this work, we explore the use
  of synthetic data generated by a VC model to assist the TTS model in
  cross-speaker style transfer tasks. Additionally, we employ pre-training of the
  style encoder using timbre perturbation and prototypical angular loss to
  mitigate speaker leakage. Our results show that using VC synthetic data can
  improve the naturalness and speaker similarity of TTS in cross-speaker
  scenarios. Furthermore, we extend this approach to a cross-language scenario,
  enhancing accent transfer.

• id: http://arxiv.org/abs/2409.17364v1

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