This work presents an extensive and detailed study on Audio-Visual Speech Recognition (AVSR) for five widely spoken languages: Chinese, Spanish, English, Arabic, and French. We have collected large-scale datasets for each language except for English, and have engaged in the training of supervised learning models. Our model, ViSpeR, is trained in a multi-lingual setting, resulting in competitive performance on newly established benchmarks for each language. The datasets and models are released to the community with an aim to serve as a foundation for triggering and feeding further research work and exploration on Audio-Visual Speech Recognition, an increasingly important area of research. Code available at \href{https://github.com/YasserdahouML/visper}{https://github.com/YasserdahouML/visper}.
@article{arxiv.2406.00038,
title = {ViSpeR: Multilingual Audio-Visual Speech Recognition},
author = {Sanath Narayan and Yasser Abdelaziz Dahou Djilali and Ankit Singh and Eustache Le Bihan and Hakim Hacid},
journal= {arXiv preprint arXiv:2406.00038},
year = {2024}
}