Improving robustness of one-shot voice conversion with deep discriminative speaker encoder

Understand

One-shot voice conversion has received significant attention since only one utterance from source speaker and target speaker respectively is required.

• Moreover, source speaker and target speaker do not need to be seen during training.
• However, available one-shot voice conversion approaches are not stable for unseen speakers as the speaker embedding extracted from one utterance of an unseen speaker is not reliable.
• In this paper, we propose a deep discriminative speaker encoder to extract speaker embedding from one utterance more effectively.