LRW-Persian: Lip-reading in the Wild Dataset for Persian Language
This addresses the problem of limited lipreading resources for Persian language researchers and applications, providing a benchmark-ready dataset for advancing multimodal speech research in underrepresented linguistic contexts.
The authors tackled the lack of non-English resources for visual speech recognition by introducing LRW-Persian, the largest in-the-wild Persian word-level lipreading dataset with over 414,000 video samples from 1,900+ hours of footage, and demonstrated its difficulty by fine-tuning two architectures to establish reference performance.
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.