# LRW-Persian: Lip-reading in the Wild Dataset for Persian Language

arXiv: https://arxiv.org/abs/2510.22716 · HTML: https://mlsanity.com/paper/2510.22716/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 24 references that are in the map.
- First posted on arXiv 26 Oct 2025.

## Builds on (most cited first, 9 of 9)

- [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)
- [TalkNet: Fully-Convolutional Non-Autoregressive Speech Synthesis Model](https://mlsanity.com/paper/2005.05514/) (2020)
- [A Multimodal German Dataset for Automatic Lip Reading Systems and Transfer Learning](https://mlsanity.com/paper/2202.13403/) (2022)
- [LRWR: Large-Scale Benchmark for Lip Reading in Russian language](https://mlsanity.com/paper/2109.06692/) (2021)
- [A Multi-Purpose Audio-Visual Corpus for Multi-Modal Persian Speech Recognition: the Arman-AV Dataset](https://mlsanity.com/paper/2301.10180/) (2023)
- [Word-level Persian Lipreading Dataset](https://mlsanity.com/paper/2304.04068/) (2023)
- [Cross-Attention Fusion of Visual and Geometric Features for Large Vocabulary Arabic Lipreading](https://mlsanity.com/paper/2402.11520/) (2024)

## Abstract (arXiv)

Lipreading has emerged as an increasingly important research area for developing robust speech recognition systems and assistive technologies for the hearing-impaired. However, non-English resources for visual speech recognition remain limited. We introduce LRW-Persian, the largest in-the-wild Persian word-level lipreading dataset, comprising 743 target words and over 414,000 video samples extracted from more than 1,900 hours of footage across 67 television programs. Designed as a benchmark-ready resource, LRW-Persian provides speaker-disjoint training and test splits, wide regional and dialectal coverage, and rich per-clip metadata including head pose, age, and gender. To ensure large-scale data quality, we establish a fully automated end-to-end curation pipeline encompassing transcription based on Automatic Speech Recognition(ASR), active-speaker localization, quality filtering, and pose/mask screening. We further fine-tune two widely used lipreading architectures on LRW-Persian, establishing reference performance and demonstrating the difficulty of Persian visual speech recognition. By filling a critical gap in low-resource languages, LRW-Persian enables rigorous benchmarking, supports cross-lingual transfer, and provides a foundation for advancing multimodal speech research in underrepresented linguistic contexts. The dataset is publicly available at: https://lrw-persian.vercel.app.