title: Styletts-VC: One-Shot Voice Conversion by Knowledge Transfer From Style-Based TTS Models
abstract: One-shot voice conversion (VC) aims to convert speech from any source speaker to an arbitrary target speaker with only a few seconds of reference speech from the target speaker. This relies heavily on disentangling the speaker's identity and speech content, a task that still remains challenging. Here, we propose a novel approach to learning disentangled speech representation by transfer learning from style-based text-to-speech (TTS) models. With cycle consistent and adversarial training, the sty
journal: 2022 IEEE Spoken Language Technology Workshop (SLT)