AGAIN-VC: A One-shot Voice Conversion using Activation Guidance and Adaptive Instance Normalization

Understand

Recently, voice conversion (VC) has been widely studied.

• Many VC systems use disentangle-based learning techniques to separate the speaker and the linguistic content information from a speech signal.
• Subsequently, they convert the voice by changing the speaker information to that of the target speaker.
• To prevent the speaker information from leaking into the content embeddings, previous works either reduce the dimension or quantize the content embedding as a strong information bottleneck.