mlsanityData to 7 Oct 2026

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

arXiv 2109.06692 ↗First posted 14 Sep 2021cs.CV cs.LGcited by 5

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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.

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 17 of its 24 references that are in the map. First posted on arXiv 14 Sep 2021.

Builds on

17 of 24 references.

References · 17

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,809
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  3. 2017mixup: Beyond Empirical Risk Minimizationcited by 5,155
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  9. 2020Lipreading using Temporal Convolutional Networkscited by 98
  10. 2017Combining Residual Networks with LSTMs for Lipreadingcited by 66
  11. 2018LRW-1000: A Naturally-Distributed Large-Scale Benchmark for Lip Reading in the Wildcited by 51
  12. 2018The PyTorch-Kaldi Speech Recognition Toolkitcited by 38
5 more
  1. 2020Deformation Flow Based Two-Stream Network for Lip Readingcited by 22
  2. 2020Learn an Effective Lip Reading Model without Painscited by 19
  3. 2019A Simple Dynamic Learning Rate Tuning Algorithm For Automated Training of DNNscited by 7
  4. 2018Towards Lipreading Sentences with Active Appearance Modelscited by 3
  5. 2020SpotFast Networks with Memory Augmented Lateral Transformers for Lipreadingcited by 3

Built on by

Cited by year

Citing papers per year: 2023: 1; 2024: 2; 2025: 2.

5 papers.

  1. 2023A Multi-Purpose Audio-Visual Corpus for Multi-Modal Persian Speech Recognition: the Arman-AV Datasetcited by 4
  2. 2024Cross-Attention Fusion of Visual and Geometric Features for Large Vocabulary Arabic Lipreadingcited by 1
  3. 2024AnnoTheia: A Semi-Automatic Annotation Toolkit for Audio-Visual Speech Technologiescited by 1
  4. 2025Evaluation of End-to-End Continuous Spanish Lipreading in Different Data Conditionscited by 0
  5. 2025LRW-Persian: Lip-reading in the Wild Dataset for Persian Languagecited by 0