Our work proposes P-Flow, a fast and data-efficient zero-shot TTS model that uses speech prompts for speaker adaptation. P-Flow comprises a speech-prompted text encoder for speaker adaptation and a flow matching generative decoder for high-quality and fast speech synthesis.

Extensive experiments on benchmarks from different DG tasks demonstrate that LFME is consistently beneficial to the baseline and can achieve comparable performance to existing arts.

Knowledge Distillation is the mechanism by which the insights gained from a larger teacher model are transferred to a smaller student model.

Then, MP A denoises the learned prompts through an auto-encoding process and aligns them by maximizing the agreement of all the reconstructed prompts.

On one hand, the local data of users may contain sensitive information, and privacy-preserving mechanisms are needed.