Generating Speakers by Prompting Listener Impressions for Pre-trained Multi-Speaker Text-to-Speech Systems

This paper proposes a speech synthesis system that allows users to specify and control the acoustic characteristics of a speaker by means of prompts describing the speaker's traits of synthesized speech. Unlike previous approaches, our method utilizes listener impressions to construct prompts, which are easier to collect and align more naturally with everyday descriptions of speaker traits. We adopt the Low-rank Adaptation (LoRA) technique to swiftly tailor a pre-trained language model to our needs, facilitating the extraction of speaker-related traits from the prompt text. Besides, different from other prompt-driven text-to-speech (TTS) systems, we separate the prompt-to-speaker module from the multi-speaker TTS system, enhancing system flexibility and compatibility with various pre-trained multi-speaker TTS systems. Moreover, for the prompt-to-speaker characteristic module, we also compared the discriminative method and flow-matching based generative method and we found that combining both methods can help the system simultaneously capture speaker-related information from prompts better and generate speech with higher fidelity.