PseudoVC：利用伪配对数据改进一次性语音转换
PseudoVC: Improving One-shot Voice Conversion with Pseudo Paired Data
AI总结：
本文提出PseudoVC方法，通过Pseudo Conversion信息扰动和Speaker Sampling策略分别解决一次性语音转换中内容编码器与说话人编码器的训练推理不匹配问题，实验表明其性能优于现有公开VC模型。
摘要：
由于一次性语音转换（VC）任务中并行训练数据稀缺，波形重建通常由各种VC系统执行。典型的一次性VC系统由内容编码器和说话人编码器组成。然而，存在两类不匹配：一类是训练与推理时内容编码器输入之间的不匹配，另一类是说话人编码器输入之间的不匹配。为解决这些不匹配问题，本文提出了一种新颖的VC训练方法，称为PseudoVC。首先，我们引入一种创新的信息扰动方法，名为Pseudo Conversion，以解决第一类不匹配问题。该方法利用预训练的VC模型将源语音转换为扰动语音，并在训练时将其输入内容编码器。其次，我们提出一种称为Speaker Sampling的方法来解决第二类不匹配问题，该方法在训练时用同一说话人的另一段语音替换说话人编码器的输入。实验结果表明，我们提出的Pseudo Conversion优于以往的信息扰动方法，并且整体PseudoVC方法超越了公开可用的VC模型。音频示例已提供。
英文摘要：
As parallel training data is scarce for one-shot voice conversion (VC) tasks, waveform reconstruction is typically performed by various VC systems. A typical one-shot VC system comprises a content encoder and a speaker encoder. However, two types of mismatches arise: one for the inputs to the content encoder during training and inference, and another for the inputs to the speaker encoder. To address these mismatches, we propose a novel VC training method called \textit{PseudoVC} in this paper. First, we introduce an innovative information perturbation approach named \textit{Pseudo Conversion} to tackle the first mismatch problem. This approach leverages pretrained VC models to convert the source utterance into a perturbed utterance, which is fed into the content encoder during training. Second, we propose an approach termed \textit{Speaker Sampling} to resolve the second mismatch problem, which will substitute the input to the speaker encoder by another utterance from the same speaker during training. Experimental results demonstrate that our proposed \textit{Pseudo Conversion} outperforms previous information perturbation methods, and the overall \textit{PseudoVC} method surpasses publicly available VC models. Audio examples are available.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
