  Recently, text-guided content generation has received extensive attention. In
this work, we explore the possibility of text description-based speaker
generation, i.e., using text prompts to control the speaker generation process.
Specifically, we propose PromptSpeaker, a text-guided speaker generation
system. PromptSpeaker consists of a prompt encoder, a zero-shot VITS, and a
Glow model, where the prompt encoder predicts a prior distribution based on the
text description and samples from this distribution to obtain a semantic
representation. The Glow model subsequently converts the semantic
representation into a speaker representation, and the zero-shot VITS finally
synthesizes the speaker's voice based on the speaker representation. We verify
that PromptSpeaker can generate speakers new from the training set by objective
metrics, and the synthetic speaker voice has reasonable subjective matching
quality with the speaker prompt.
