# Learning Speaker-specific Lip-to-Speech Generation

arXiv: https://arxiv.org/abs/2206.02050 · HTML: https://mlsanity.com/paper/2206.02050/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 41 of its 65 references that are in the map.
- First posted on arXiv 4 Jun 2022.

## Builds on (most cited first, 30 of 41)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://mlsanity.com/paper/1910.13461/) (2019)
- [Going Deeper with Convolutions](https://mlsanity.com/paper/1409.4842/) (2014)
- [Auto-Encoding Variational Bayes](https://mlsanity.com/paper/1312.6114/) (2013)
- [ALBERT: A Lite BERT for Self-supervised Learning of Language Representations](https://mlsanity.com/paper/1909.11942/) (2019)
- [Conformer: Convolution-augmented Transformer for Speech Recognition](https://mlsanity.com/paper/2005.08100/) (2020)
- [Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context](https://mlsanity.com/paper/1901.02860/) (2019)
- [Generating Sequences With Recurrent Neural Networks](https://mlsanity.com/paper/1308.0850/) (2013)
- [Action-Conditioned 3D Human Motion Synthesis with Transformer VAE](https://mlsanity.com/paper/2104.05670/) (2021)
- [Multi-modal Transformer for Video Retrieval](https://mlsanity.com/paper/2007.10639/) (2020)
- [Deep Audio-Visual Speech Recognition](https://mlsanity.com/paper/1809.02108/) (2018)
- [LRS3-TED: a large-scale dataset for visual speech recognition](https://mlsanity.com/paper/1809.00496/) (2018)
- [Pixel-BERT: Aligning Image Pixels with Text by Deep Multi-Modal Transformers](https://mlsanity.com/paper/2004.00849/) (2020)
- [Lip Reading Sentences in the Wild](https://mlsanity.com/paper/1611.05358/) (2016)
- [The Conversation: Deep Audio-Visual Speech Enhancement](https://mlsanity.com/paper/1804.04121/) (2018)
- [ASR is all you need: cross-modal distillation for lip reading](https://mlsanity.com/paper/1911.12747/) (2019)
- [LipNet: End-to-End Sentence-level Lipreading](https://mlsanity.com/paper/1611.01599/) (2016)
- [Learning Individual Speaking Styles for Accurate Lip to Speech Synthesis](https://mlsanity.com/paper/2005.08209/) (2020)
- [Combining Residual Networks with LSTMs for Lipreading](https://mlsanity.com/paper/1703.04105/) (2017)
- [Vid2speech: Speech Reconstruction from Silent Video](https://mlsanity.com/paper/1701.00495/) (2017)
- [Transformer-based Conditional Variational Autoencoder for Controllable Story Generation](https://mlsanity.com/paper/2101.00828/) (2021)
- [Vision-Infused Deep Audio Inpainting](https://mlsanity.com/paper/1910.10997/) (2019)
- [Variational Transformers for Diverse Response Generation](https://mlsanity.com/paper/2003.12738/) (2020)
- [Lip2AudSpec: Speech reconstruction from silent lip movements video](https://mlsanity.com/paper/1710.09798/) (2017)
- [Coordinated Joint Multimodal Embeddings for Generalized Audio-Visual Zeroshot Classification and Retrieval of Videos](https://mlsanity.com/paper/1910.08732/) (2019)
- [Improved Speech Reconstruction from Silent Video](https://mlsanity.com/paper/1708.01204/) (2017)

## Built on by (most cited first, 3 of 3)

- [NaturalL2S: End-to-End High-quality Multispeaker Lip-to-Speech Synthesis with Differential Digital Signal Processing](https://mlsanity.com/paper/2502.12002/) (2025)
- [RobustL2S: Speaker-Specific Lip-to-Speech Synthesis exploiting Self-Supervised Representations](https://mlsanity.com/paper/2307.01233/) (2023)
- [MoDA: Multi-modal Diffusion Architecture for Talking Head Generation](https://mlsanity.com/paper/2507.03256/) (2025)

## Abstract (arXiv)

Understanding the lip movement and inferring the speech from it is notoriously difficult for the common person. The task of accurate lip-reading gets help from various cues of the speaker and its contextual or environmental setting. Every speaker has a different accent and speaking style, which can be inferred from their visual and speech features. This work aims to understand the correlation/mapping between speech and the sequence of lip movement of individual speakers in an unconstrained and large vocabulary. We model the frame sequence as a prior to the transformer in an auto-encoder setting and learned a joint embedding that exploits temporal properties of both audio and video. We learn temporal synchronization using deep metric learning, which guides the decoder to generate speech in sync with input lip movements. The predictive posterior thus gives us the generated speech in speaker speaking style. We have trained our model on the Grid and Lip2Wav Chemistry lecture dataset to evaluate single speaker natural speech generation tasks from lip movement in an unconstrained natural setting. Extensive evaluation using various qualitative and quantitative metrics with human evaluation also shows that our method outperforms the Lip2Wav Chemistry dataset(large vocabulary in an unconstrained setting) by a good margin across almost all evaluation metrics and marginally outperforms the state-of-the-art on GRID dataset.