mlsanityData to 7 Oct 2026

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

arXiv 2111.10139 ↗First posted 19 Nov 2021cs.CV cs.CLcited by 12

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Abstract (arXiv)

In this paper we present VDTTS, a Visually-Driven Text-to-Speech model. Motivated by dubbing, VDTTS takes advantage 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, approaching the video-speech synchronization quality of the ground-truth, on several challenging benchmarks including "in-the-wild" content from VoxCeleb2. Supplementary demo videos demonstrating video-speech synchronization, robustness to speaker ID swapping, and prosody, presented at the project page.

Key facts. Cited by 12 papers in the map (data through 7 Oct 2026). Builds on 45 of its 62 references that are in the map. First posted on arXiv 19 Nov 2021.

Builds on

45 of 62 references.

References · 45

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33 more
  1. 2019MelGAN: Generative Adversarial Networks for Conditional Waveform Synthesiscited by 351
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  8. 2018Towards End-to-End Prosody Transfer for Expressive Speech Synthesis with Tacotroncited by 171
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  22. 2020Parallel Tacotron: Non-Autoregressive and Controllable TTScited by 49
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  26. 2021Neural Dubber: Dubbing for Videos According to Scriptscited by 39
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  28. 2021Parallel Tacotron 2: A Non-Autoregressive Neural TTS Model with Differentiable Duration Modelingcited by 31
  29. 2021End-to-End Video-To-Speech Synthesis using Generative Adversarial Networkscited by 27
  30. 2019Lipper: Synthesizing Thy Speech using Multi-View Lipreadingcited by 19
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  33. 2020Textual Echo Cancellationcited by 2

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Cited by year

Citing papers per year: 2022: 3; 2023: 1; 2024: 4; 2025: 4.

12 papers.