'Voice Conversion' paper candidate 2410.15499

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article info.

• title: Improving Voice Quality in Speech Anonymization With Just Perception-Informed Losses

• summary: The increasing use of cloud-based speech assistants has heightened the need
  for effective speech anonymization, which aims to obscure a speaker's identity
  while retaining critical information for subsequent tasks. One approach to
  achieving this is through voice conversion. While existing methods often
  emphasize complex architectures and training techniques, our research
  underscores the importance of loss functions inspired by the human auditory
  system. Our proposed loss functions are model-agnostic, incorporating
  handcrafted and deep learning-based features to effectively capture quality
  representations. Through objective and subjective evaluations, we demonstrate
  that a VQVAE-based model, enhanced with our perception-driven losses, surpasses
  the vanilla model in terms of naturalness, intelligibility, and prosody while
  maintaining speaker anonymity. These improvements are consistently observed
  across various datasets, languages, target speakers, and genders.

• id: http://arxiv.org/abs/2410.15499v1

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