标题： FastVoiceGrad：基于一步扩散的对抗条件扩散蒸馏的语音转换方法 显示英文标题

标题： FastVoiceGrad: One-step Diffusion-Based Voice Conversion with Adversarial Conditional Diffusion Distillation

摘要： 基于扩散的语音转换（VC）技术，例如 VoiceGrad，因其在语音质量和说话人相似度方面的高性能而引起了兴趣。 然而，一个显著的局限性是多步反向扩散导致的推理速度缓慢。 因此，我们提出了 FastVoiceGrad，这是一种新颖的一步式扩散基语音转换方法，在将迭代次数从几十减少到一的同时继承了多步扩散基语音转换的高性能。 我们通过对抗条件扩散蒸馏（ACDD）获得该模型，利用生成对抗网络和扩散模型的能力，并重新考虑采样中的初始状态。 一次性的任意到任意语音转换评估表明，FastVoiceGrad 在语音转换性能上优于或可与先前的多步扩散基语音转换相媲美，同时提升了推理速度。 音频样本可在 https://www.kecl.ntt.co.jp/people/kaneko.takuhiro/projects/fastvoicegrad/ 获取。 显示英文摘要

摘要： 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/.