On the Semantic Latent Space of Diffusion-Based Text-to-Speech Models
Miri Varshavsky-Hassid Roy Hirsch Regev Cohen Tomer Golany Daniel Freedman Ehud Rivlin
The incorporation of Denoising Diffusion Models (DDMs) in the Text-to-Speech
(TTS) domain is rising, providing great value in synthesizing high quality
speech. Although they exhibit impressive audio quality, the extent of their
semantic capabilities is unknown, and controlling their synthesized speech's
vocal properties remains a challenge. Inspired by recent advances in image
synthesis, we explore the latent space of frozen TTS models, which is composed
of the latent bottleneck activations of the DDM's denoiser. We identify that
this space contains rich semantic information, and outline several novel
methods for finding semantic directions within it, both supervised and
unsupervised. We then demonstrate how these enable off-the-shelf audio editing,
without any further training, architectural changes or data requirements. We
present evidence of the semantic and acoustic qualities of the edited audio,
and provide supplemental samples:
https://latent-analysis-grad-tts.github.io/speech-samples/.