More than Words: In-the-Wild Visually-Driven Prosody for Text-to-Speech

  title={More than Words: In-the-Wild Visually-Driven Prosody for Text-to-Speech},
  author={Michael Hassid and Michelle Tadmor Ramanovich and Brendan Shillingford and Miaosen Wang and Ye Jia and Tal Remez},
  journal={2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
In this paper we present VDTTS, a Visually-Driven Text-to-Speech model. Motivated by dubbing, VDTTS takes ad-vantage of video frames as an additional input alongside text, and generates speech that matches the video signal. We demonstrate how this allows VDTTS to, unlike plain TTS models, generate speech that not only has prosodic variations like natural pauses and pitch, but is also synchronized to the input video. Experimentally, we show our model produces well-synchronized outputs… 

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