# LRWR: Large-Scale Benchmark for Lip Reading in Russian language

arXiv: https://arxiv.org/abs/2109.06692 · HTML: https://mlsanity.com/paper/2109.06692/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 24 references that are in the map.
- First posted on arXiv 14 Sep 2021.

## Builds on (most cited first, 17 of 17)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Rethinking the Inception Architecture for Computer Vision](https://mlsanity.com/paper/1512.00567/) (2015)
- [mixup: Beyond Empirical Risk Minimization](https://mlsanity.com/paper/1710.09412/) (2017)
- [Improved Regularization of Convolutional Neural Networks with Cutout](https://mlsanity.com/paper/1708.04552/) (2017)
- [A Closer Look at Spatiotemporal Convolutions for Action Recognition](https://mlsanity.com/paper/1711.11248/) (2017)
- [How far are we from solving the 2D & 3D Face Alignment problem? (and a dataset of 230,000 3D facial landmarks)](https://mlsanity.com/paper/1703.07332/) (2017)
- [ResNeSt: Split-Attention Networks](https://mlsanity.com/paper/2004.08955/) (2020)
- [Video Classification with Channel-Separated Convolutional Networks](https://mlsanity.com/paper/1904.02811/) (2019)
- [Lipreading using Temporal Convolutional Networks](https://mlsanity.com/paper/2001.08702/) (2020)
- [Combining Residual Networks with LSTMs for Lipreading](https://mlsanity.com/paper/1703.04105/) (2017)
- [LRW-1000: A Naturally-Distributed Large-Scale Benchmark for Lip Reading in the Wild](https://mlsanity.com/paper/1810.06990/) (2018)
- [The PyTorch-Kaldi Speech Recognition Toolkit](https://mlsanity.com/paper/1811.07453/) (2018)
- [Deformation Flow Based Two-Stream Network for Lip Reading](https://mlsanity.com/paper/2003.05709/) (2020)
- [Learn an Effective Lip Reading Model without Pains](https://mlsanity.com/paper/2011.07557/) (2020)
- [A Simple Dynamic Learning Rate Tuning Algorithm For Automated Training of DNNs](https://mlsanity.com/paper/1910.11605/) (2019)
- [Towards Lipreading Sentences with Active Appearance Models](https://mlsanity.com/paper/1805.11688/) (2018)
- [SpotFast Networks with Memory Augmented Lateral Transformers for Lipreading](https://mlsanity.com/paper/2005.10903/) (2020)

## Built on by (most cited first, 5 of 5)

- [A Multi-Purpose Audio-Visual Corpus for Multi-Modal Persian Speech Recognition: the Arman-AV Dataset](https://mlsanity.com/paper/2301.10180/) (2023)
- [Cross-Attention Fusion of Visual and Geometric Features for Large Vocabulary Arabic Lipreading](https://mlsanity.com/paper/2402.11520/) (2024)
- [AnnoTheia: A Semi-Automatic Annotation Toolkit for Audio-Visual Speech Technologies](https://mlsanity.com/paper/2402.13152/) (2024)
- [Evaluation of End-to-End Continuous Spanish Lipreading in Different Data Conditions](https://mlsanity.com/paper/2502.00464/) (2025)
- [LRW-Persian: Lip-reading in the Wild Dataset for Persian Language](https://mlsanity.com/paper/2510.22716/) (2025)

## Abstract (arXiv)

Lipreading, also known as visual speech recognition, aims to identify the speech content from videos by analyzing the visual deformations of lips and nearby areas. One of the significant obstacles for research in this field is the lack of proper datasets for a wide variety of languages: so far, these methods have been focused only on English or Chinese. In this paper, we introduce a naturally distributed large-scale benchmark for lipreading in Russian language, named LRWR, which contains 235 classes and 135 speakers. We provide a detailed description of the dataset collection pipeline and dataset statistics. We also present a comprehensive comparison of the current popular lipreading methods on LRWR and conduct a detailed analysis of their performance. The results demonstrate the differences between the benchmarked languages and provide several promising directions for lipreading models finetuning. Thanks to our findings, we also achieved new state-of-the-art results on the LRW benchmark.