Non-Parallel Voice Conversion Using Cycle-Consistent Adversarial Networks with Self-Supervised Representations
Chanjun Chun, Young Han Lee, Geon Woo Lee, Moongu Jeon, Hong Kook Kim · 2023
Numerous voice conversion techniques using non-parallel data have been presented. Among these, there are many algorithms related to style transfer. This is because the voice conversion problem can be determined as a style transfer problem, where the linguistic and speaker information can be regarded as domains and styles, respectively. Here, the group of CycleGAN-VC series has considerable achievement, and thus we examine the feasibility of CycleGAN-VC for self-supervised representations. In other words, we incorporate analysis features extracted from wav2vec into the CycleGAN-VC model. Objective experiments showed that the quality of the converted speech is comparable to that of the original speech, and the source speech was successfully transformed into the voice of the target speech while preserving the linguistic information.