# High-Quality Automatic Voice Over with Accurate Alignment: Supervision through Self-Supervised Discrete Speech Units

arXiv: https://arxiv.org/abs/2306.17005 · HTML: https://mlsanity.com/paper/2306.17005/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 32 of its 38 references that are in the map.
- First posted on arXiv 29 Jun 2023.

## Builds on (most cited first, 30 of 32)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations](https://mlsanity.com/paper/2006.11477/) (2020)
- [HuBERT: Self-Supervised Speech Representation Learning by Masked Prediction of Hidden Units](https://mlsanity.com/paper/2106.07447/) (2021)
- [WaveNet: A Generative Model for Raw Audio](https://mlsanity.com/paper/1609.03499/) (2016)
- [HiFi-GAN: Generative Adversarial Networks for Efficient and High Fidelity Speech Synthesis](https://mlsanity.com/paper/2010.05646/) (2020)
- [VoxCeleb2: Deep Speaker Recognition](https://mlsanity.com/paper/1806.05622/) (2018)
- [Natural TTS Synthesis by Conditioning WaveNet on Mel Spectrogram Predictions](https://mlsanity.com/paper/1712.05884/) (2017)
- [FastSpeech 2: Fast and High-Quality End-to-End Text to Speech](https://mlsanity.com/paper/2006.04558/) (2020)
- [A Lip Sync Expert Is All You Need for Speech to Lip Generation In The Wild](https://mlsanity.com/paper/2008.10010/) (2020)
- [Tacotron: Towards End-to-End Speech Synthesis](https://mlsanity.com/paper/1703.10135/) (2017)
- [MelGAN: Generative Adversarial Networks for Conditional Waveform Synthesis](https://mlsanity.com/paper/1910.06711/) (2019)
- [LRS3-TED: a large-scale dataset for visual speech recognition](https://mlsanity.com/paper/1809.00496/) (2018)
- [Layer-wise Analysis of a Self-supervised Speech Representation Model](https://mlsanity.com/paper/2107.04734/) (2021)
- [Learning Audio-Visual Speech Representation by Masked Multimodal Cluster Prediction](https://mlsanity.com/paper/2201.02184/) (2022)
- [Speech Resynthesis from Discrete Disentangled Self-Supervised Representations](https://mlsanity.com/paper/2104.00355/) (2021)
- [A Survey on Neural Speech Synthesis](https://mlsanity.com/paper/2106.15561/) (2021)
- [Direct speech-to-speech translation with discrete units](https://mlsanity.com/paper/2107.05604/) (2021)
- [An Overview of Voice Conversion and its Challenges: From Statistical Modeling to Deep Learning](https://mlsanity.com/paper/2008.03648/) (2020)
- [Deep Model Reassembly](https://mlsanity.com/paper/2210.17409/) (2022)
- [Learning Individual Speaking Styles for Accurate Lip to Speech Synthesis](https://mlsanity.com/paper/2005.08209/) (2020)
- [A Comparison of Discrete and Soft Speech Units for Improved Voice Conversion](https://mlsanity.com/paper/2111.02392/) (2021)
- [Factorizing Knowledge in Neural Networks](https://mlsanity.com/paper/2207.03337/) (2022)
- [MultiSpeech: Multi-Speaker Text to Speech with Transformer](https://mlsanity.com/paper/2006.04664/) (2020)
- [Neural Dubber: Dubbing for Videos According to Scripts](https://mlsanity.com/paper/2110.08243/) (2021)
- [VQTTS: High-Fidelity Text-to-Speech Synthesis with Self-Supervised VQ Acoustic Feature](https://mlsanity.com/paper/2204.00768/) (2022)
- [Muse: Multi-modal target speaker extraction with visual cues](https://mlsanity.com/paper/2010.07775/) (2020)
- [Any-to-One Sequence-to-Sequence Voice Conversion using Self-Supervised Discrete Speech Representations](https://mlsanity.com/paper/2010.12231/) (2020)
- [Selective Listening by Synchronizing Speech with Lips](https://mlsanity.com/paper/2106.07150/) (2021)
- [VisualTTS: TTS with Accurate Lip-Speech Synchronization for Automatic Voice Over](https://mlsanity.com/paper/2110.03342/) (2021)
- [More than Words: In-the-Wild Visually-Driven Prosody for Text-to-Speech](https://mlsanity.com/paper/2111.10139/) (2021)

## Built on by (most cited first, 4 of 4)

- [MCDubber: Multimodal Context-Aware Expressive Video Dubbing](https://mlsanity.com/paper/2408.11593/) (2024)
- [Towards Expressive Video Dubbing with Multiscale Multimodal Context Interaction](https://mlsanity.com/paper/2412.18748/) (2024)
- [VSSFlow: Unifying Video-conditioned Sound and Speech Generation via Joint Learning](https://mlsanity.com/paper/2509.24773/) (2025)
- [Converting Anyone's Voice: End-to-End Expressive Voice Conversion with a Conditional Diffusion Model](https://mlsanity.com/paper/2405.01730/) (2024)

## Abstract (arXiv)

The goal of Automatic Voice Over (AVO) is to generate speech in sync with a silent video given its text script. Recent AVO frameworks built upon text-to-speech synthesis (TTS) have shown impressive results. However, the current AVO learning objective of acoustic feature reconstruction brings in indirect supervision for inter-modal alignment learning, thus limiting the synchronization performance and synthetic speech quality. To this end, we propose a novel AVO method leveraging the learning objective of self-supervised discrete speech unit prediction, which not only provides more direct supervision for the alignment learning, but also alleviates the mismatch between the text-video context and acoustic features. Experimental results show that our proposed method achieves remarkable lip-speech synchronization and high speech quality by outperforming baselines in both objective and subjective evaluations. Code and speech samples are publicly available.