# Word-level Persian Lipreading Dataset

arXiv: https://arxiv.org/abs/2304.04068 · HTML: https://mlsanity.com/paper/2304.04068/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 23 references that are in the map.
- First posted on arXiv 8 Apr 2023.

## Builds on (most cited first, 12 of 12)

- [LRS3-TED: a large-scale dataset for visual speech recognition](https://mlsanity.com/paper/1809.00496/) (2018)
- [Lip Reading Sentences in the Wild](https://mlsanity.com/paper/1611.05358/) (2016)
- [S3FD: Single Shot Scale-invariant Face Detector](https://mlsanity.com/paper/1708.05237/) (2017)
- [Is Someone Speaking? Exploring Long-term Temporal Features for Audio-visual Active Speaker Detection](https://mlsanity.com/paper/2107.06592/) (2021)
- [Lipreading using Temporal Convolutional Networks](https://mlsanity.com/paper/2001.08702/) (2020)
- [AVA-ActiveSpeaker: An Audio-Visual Dataset for Active Speaker Detection](https://mlsanity.com/paper/1901.01342/) (2019)
- [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)
- [Towards Practical Lipreading with Distilled and Efficient Models](https://mlsanity.com/paper/2007.06504/) (2020)
- [Distinguishing Homophenes Using Multi-Head Visual-Audio Memory for Lip Reading](https://mlsanity.com/paper/2204.01725/) (2022)
- [A Multimodal German Dataset for Automatic Lip Reading Systems and Transfer Learning](https://mlsanity.com/paper/2202.13403/) (2022)
- [Lip reading using external viseme decoding](https://mlsanity.com/paper/2104.04784/) (2021)

## Built on by (most cited first, 2 of 2)

- [Leveraging Visemes for Better Visual Speech Representation and Lip Reading](https://mlsanity.com/paper/2307.10157/) (2023)
- [LRW-Persian: Lip-reading in the Wild Dataset for Persian Language](https://mlsanity.com/paper/2510.22716/) (2025)

## Abstract (arXiv)

Lip-reading has made impressive progress in recent years, driven by advances in deep learning. Nonetheless, the prerequisite such advances is a suitable dataset. This paper provides a new in-the-wild dataset for Persian word-level lipreading containing 244,000 videos from approximately 1,800 speakers. We evaluated the state-of-the-art method in this field and used a novel approach for word-level lip-reading. In this method, we used the AV-HuBERT model for feature extraction and obtained significantly better performance on our dataset.