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EdiTTS: Score-based Editing for Controllable Text-to-Speech. (arXiv:2110.02584v2 [cs.SD] UPDATED)
Feb. 3, 2022, 2:11 a.m. | Jaesung Tae, Hyeongju Kim, Taesu Kim
cs.LG updates on arXiv.org arxiv.org
We present EdiTTS, an off-the-shelf speech editing methodology based on
score-based generative modeling for text-to-speech synthesis. EdiTTS allows for
targeted, granular editing of audio, both in terms of content and pitch,
without the need for any additional training, task-specific optimization, or
architectural modifications to the score-based model backbone. Specifically, we
apply coarse yet deliberate perturbations in the Gaussian prior space to induce
desired behavior from the diffusion model, while applying masks and softening
kernels to ensure that iterative edits are …
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