# LRW-1000: A Naturally-Distributed Large-Scale Benchmark for Lip Reading in the Wild

arXiv: https://arxiv.org/abs/1810.06990 · HTML: https://mlsanity.com/paper/1810.06990/

## Key facts

- Cited by 51 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Oct 2018.

## Built on by (most cited first, 30 of 51)

- [Distinguishing Homophenes Using Multi-Head Visual-Audio Memory for Lip Reading](https://mlsanity.com/paper/2204.01725/) (2022)
- [Deep Learning for Visual Speech Analysis: A Survey](https://mlsanity.com/paper/2205.10839/) (2022)
- [Multi-modality Associative Bridging through Memory: Speech Sound Recollected from Face Video](https://mlsanity.com/paper/2204.01265/) (2022)
- [Speaker-adaptive Lip Reading with User-dependent Padding](https://mlsanity.com/paper/2208.04498/) (2022)
- [Lip Reading for Low-resource Languages by Learning and Combining General Speech Knowledge and Language-specific Knowledge](https://mlsanity.com/paper/2308.09311/) (2023)
- [MixSpeech: Cross-Modality Self-Learning with Audio-Visual Stream Mixup for Visual Speech Translation and Recognition](https://mlsanity.com/paper/2303.05309/) (2023)
- [SyncVSR: Data-Efficient Visual Speech Recognition with End-to-End Crossmodal Audio Token Synchronization](https://mlsanity.com/paper/2406.12233/) (2024)
- [A Study of Dropout-Induced Modality Bias on Robustness to Missing Video Frames for Audio-Visual Speech Recognition](https://mlsanity.com/paper/2403.04245/) (2024)
- [LipSound2: Self-Supervised Pre-Training for Lip-to-Speech Reconstruction and Lip Reading](https://mlsanity.com/paper/2112.04748/) (2021)
- [Multi-Temporal Lip-Audio Memory for Visual Speech Recognition](https://mlsanity.com/paper/2305.04542/) (2023)
- [From Pixels to Portraits: A Comprehensive Survey of Talking Head Generation Techniques and Applications](https://mlsanity.com/paper/2308.16041/) (2023)
- [A Multimodal German Dataset for Automatic Lip Reading Systems and Transfer Learning](https://mlsanity.com/paper/2202.13403/) (2022)
- [MMFace4D: A Large-Scale Multi-Modal 4D Face Dataset for Audio-Driven 3D Face Animation](https://mlsanity.com/paper/2303.09797/) (2023)
- [Emotional Talking Head Generation based on Memory-Sharing and Attention-Augmented Networks](https://mlsanity.com/paper/2306.03594/) (2023)
- [Phoneme-Level Visual Speech Recognition via Point-Visual Fusion and Language Model Reconstruction](https://mlsanity.com/paper/2507.18863/) (2025)
- [The DeepSpeak Dataset](https://mlsanity.com/paper/2408.05366/) (2024)
- [Advancing Talking Head Generation: A Comprehensive Survey of Multi-Modal Methodologies, Datasets, Evaluation Metrics, and Loss Functions](https://mlsanity.com/paper/2507.02900/) (2025)
- [LRWR: Large-Scale Benchmark for Lip Reading in Russian language](https://mlsanity.com/paper/2109.06692/) (2021)
- [Landmark-Guided Cross-Speaker Lip Reading with Mutual Information Regularization](https://mlsanity.com/paper/2403.16071/) (2024)
- [CNVSRC 2023: The First Chinese Continuous Visual Speech Recognition Challenge](https://mlsanity.com/paper/2406.10313/) (2024)
- [A Comprehensive Taxonomy and Analysis of Talking Head Synthesis: Techniques for Portrait Generation, Driving Mechanisms, and Editing](https://mlsanity.com/paper/2406.10553/) (2024)
- [Personalized Lip Reading: Adapting to Your Unique Lip Movements with Vision and Language](https://mlsanity.com/paper/2409.00986/) (2024)
- [Chinese-LiPS: A Chinese audio-visual speech recognition dataset with Lip-reading and Presentation Slides](https://mlsanity.com/paper/2504.15066/) (2025)
- [Advances and Challenges in Deep Lip Reading](https://mlsanity.com/paper/2110.07879/) (2021)
- [A Multi-Purpose Audio-Visual Corpus for Multi-Modal Persian Speech Recognition: the Arman-AV Dataset](https://mlsanity.com/paper/2301.10180/) (2023)
- [OLKAVS: An Open Large-Scale Korean Audio-Visual Speech Dataset](https://mlsanity.com/paper/2301.06375/) (2023)
- [Improving Audio-Visual Speech Recognition by Lip-Subword Correlation Based Visual Pre-training and Cross-Modal Fusion Encoder](https://mlsanity.com/paper/2308.08488/) (2023)
- [A Survey on Deep Multi-modal Learning for Body Language Recognition and Generation](https://mlsanity.com/paper/2308.08849/) (2023)
- [Word-level Persian Lipreading Dataset](https://mlsanity.com/paper/2304.04068/) (2023)
- [Leveraging Visemes for Better Visual Speech Representation and Lip Reading](https://mlsanity.com/paper/2307.10157/) (2023)

## Abstract (arXiv)

Large-scale datasets have successively proven their fundamental importance in several research fields, especially for early progress in some emerging topics. In this paper, we focus on the problem of visual speech recognition, also known as lipreading, which has received increasing interest in recent years. We present a naturally-distributed large-scale benchmark for lip reading in the wild, named LRW-1000, which contains 1,000 classes with 718,018 samples from more than 2,000 individual speakers. Each class corresponds to the syllables of a Mandarin word composed of one or several Chinese characters. To the best of our knowledge, it is currently the largest word-level lipreading dataset and also the only public large-scale Mandarin lipreading dataset. This dataset aims at covering a "natural" variability over different speech modes and imaging conditions to incorporate challenges encountered in practical applications. It has shown a large variation in this benchmark in several aspects, including the number of samples in each class, video resolution, lighting conditions, and speakers' attributes such as pose, age, gender, and make-up. Besides providing a detailed description of the dataset and its collection pipeline, we evaluate several typical popular lipreading methods and perform a thorough analysis of the results from several aspects. The results demonstrate the consistency and challenges of our dataset, which may open up some new promising directions for future work.