FastVoiceGrad: One-step Diffusion-Based Voice Conversion with
  Adversarial Conditional Diffusion Distillation
Authors: name: Takuhiro Kaneko, name: Hirokazu Kameoka, name: Kou Tanaka, name: Yuto Kondo

Diffusion-based voice conversion (VC) techniques such as VoiceGrad have
attracted interest because of their high VC performance in terms of speech
quality and speaker similarity. However, a notable limitation is the slow
inference caused by the multi-step reverse diffusion. Therefore, we propose
FastVoiceGrad, a novel one-step diffusion-based VC that reduces the number of
iterations from dozens to one while inheriting the high VC performance of the
multi-step diffusion-based VC. We obtain the model using adversarial
conditional diffusion distillation (ACDD), leveraging the ability of generative
adversarial networks and diffusion models while reconsidering the initial
states in sampling. Evaluations of one-shot any-to-any VC demonstrate that
FastVoiceGrad achieves VC performance superior to or comparable to that of
previous multi-step diffusion-based VC while enhancing the inference speed.
Audio samples are available at
https://www.kecl.ntt.co.jp/people/kaneko.takuhiro/projects/fastvoicegrad/.