# Enhancing Lip Reading with Multi-Scale Video and Multi-Encoder

arXiv: https://arxiv.org/abs/2404.05466 · HTML: https://mlsanity.com/paper/2404.05466/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 24 references that are in the map.
- First posted on arXiv 8 Apr 2024.

## Builds on (most cited first, 15 of 15)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Conformer: Convolution-augmented Transformer for Speech Recognition](https://mlsanity.com/paper/2005.08100/) (2020)
- [ESPnet: End-to-End Speech Processing Toolkit](https://mlsanity.com/paper/1804.00015/) (2018)
- [Joint CTC-Attention based End-to-End Speech Recognition using Multi-task Learning](https://mlsanity.com/paper/1609.06773/) (2016)
- [End-to-end Audio-visual Speech Recognition with Conformers](https://mlsanity.com/paper/2102.06657/) (2021)
- [Auto-AVSR: Audio-Visual Speech Recognition with Automatic Labels](https://mlsanity.com/paper/2303.14307/) (2023)
- [E-Branchformer: Branchformer with Enhanced merging for speech recognition](https://mlsanity.com/paper/2210.00077/) (2022)
- [Branchformer: Parallel MLP-Attention Architectures to Capture Local and Global Context for Speech Recognition and Understanding](https://mlsanity.com/paper/2207.02971/) (2022)
- [Achieving Human Parity in Conversational Speech Recognition](https://mlsanity.com/paper/1610.05256/) (2016)
- [Transformer-Based Video Front-Ends for Audio-Visual Speech Recognition for Single and Multi-Person Video](https://mlsanity.com/paper/2201.10439/) (2022)
- [Can We Read Speech Beyond the Lips? Rethinking RoI Selection for Deep Visual Speech Recognition](https://mlsanity.com/paper/2003.03206/) (2020)
- [MLCA-AVSR: Multi-Layer Cross Attention Fusion based Audio-Visual Speech Recognition](https://mlsanity.com/paper/2401.03424/) (2024)
- [The Multimodal Information based Speech Processing (MISP) 2022 Challenge: Audio-Visual Diarization and Recognition](https://mlsanity.com/paper/2303.06326/) (2023)
- [The NPU-ASLP-LiAuto System Description for Visual Speech Recognition in CNVSRC 2023](https://mlsanity.com/paper/2401.06788/) (2024)
- [The Multimodal Information Based Speech Processing (MISP) 2023 Challenge: Audio-Visual Target Speaker Extraction](https://mlsanity.com/paper/2309.08348/) (2023)

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

- [Target Speaker Lipreading by Audio-Visual Self-Distillation Pretraining and Speaker Adaptation](https://mlsanity.com/paper/2502.05758/) (2025)
- [CNVSRC 2024: The Second Chinese Continuous Visual Speech Recognition Challenge](https://mlsanity.com/paper/2506.02010/) (2025)
- [The NPU-ASLP System Description for Visual Speech Recognition in CNVSRC 2024](https://mlsanity.com/paper/2408.02369/) (2024)
- [Learning Visual Speech Representations via Cross-Modal Distillation and Joint Face-Lip Modeling](https://mlsanity.com/paper/2610.19559/) (2026)

## Abstract (arXiv)

Automatic lip-reading (ALR) aims to automatically transcribe spoken content from a speaker's silent lip motion captured in video. Current mainstream lip-reading approaches only use a single visual encoder to model input videos of a single scale. In this paper, we propose to enhance lip-reading by incorporating multi-scale video data and multi-encoder. Specifically, we first propose a novel multi-scale lip motion extraction algorithm based on the size of the speaker's face and an Enhanced ResNet3D visual front-end (VFE) to extract lip features at different scales. For the multi-encoder, in addition to the mainstream Transformer and Conformer, we also incorporate the recently proposed Branchformer and E-Branchformer as visual encoders. In the experiments, we explore the influence of different video data scales and encoders on ALR system performance and fuse the texts transcribed by all ALR systems using recognizer output voting error reduction (ROVER). Finally, our proposed approach placed second in the ICME 2024 ChatCLR Challenge Task 2, with a 21.52% reduction in character error rate (CER) compared to the official baseline on the evaluation set.