论文标题
可逆语音转换
Invertible Voice Conversion
论文作者
论文摘要
在本文中，我们提出了一个可演变的深度学习框架，称为Invvc，用于语音转换。它是针对语音转换系统固有的可能威胁而设计的。具体而言，我们开发了一个可逆框架，使源标识可追溯。该框架建立在一系列可逆的$ 1 \ times1 $卷积和由仿射耦合层组成的流动。我们将提出的框架应用于一对一的语音转换，并使用并行训练数据将其多对一转换应用于一对一的转换。实验结果表明，这种方法在语音转换上产生了令人印象深刻的性能，此外，使用与转发相同的参数，转换后的结果可以逆转回源输入。
In this paper, we propose an invertible deep learning framework called INVVC for voice conversion. It is designed against the possible threats that inherently come along with voice conversion systems. Specifically, we develop an invertible framework that makes the source identity traceable. The framework is built on a series of invertible $1\times1$ convolutions and flows consisting of affine coupling layers. We apply the proposed framework to one-to-one voice conversion and many-to-one conversion using parallel training data. Experimental results show that this approach yields impressive performance on voice conversion and, moreover, the converted results can be reversed back to the source inputs utilizing the same parameters as in forwarding.
