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There has been a significant progress in Text-To-Speech (TTS) synthesistechnology in recent years, thanks to the advancement in neural generativemodeling. However, existing methods on any-speaker adaptive TTS have achievedunsatisfactory performance, due to their suboptimal accuracy in mimicking thetarget speakers' styles. In this work, we present Grad-StyleSpeech, which is anany-speaker adaptive TTS framework that is based on a diffusion model that cangenerate highly natural speech with extremely high similarity to targetspeakers' voice, given a few seconds of reference speech. Grad-StyleSpeechsignificantly outperforms recent speaker-adaptive TTS baselines on Englishbenchmarks. Audio samples are available athttps://nardien.github.io/grad-stylespeech-demo.

Grad-StyleSpeech: Any-speaker Adaptive Text-to-Speech Synthesis with Diffusion Models