A Review of Lip-Reading: From Datasets and Architectures to Cross-Linguistic Performance Gaps

Abstract

This paper aims to systematically review deep learning–driven lip-reading technologies, covering benchmark datasets, advances in model architecture, and cross-linguistic performance. Distinguished from existing surveys that predominantly enumerate methodologies in chronological sequence, this work consolidates the most extensive collection of 79 lip-reading datasets reported to date and establishes a problem-driven analytical framework to delineate the technological evolution from shallow feature extraction to global temporal modeling. Systematic analysis of the state of the art reveals a pronounced cross-linguistic performance disparity: state-of-the-art visual-only models attain 94.1% accuracy on standard English benchmarks, yet only 57.1% accuracy on Chinese benchmarks, representing a substantial performance gap of 37 percentage points rooted in intrinsic linguistic properties. This review identifies critical challenges and outlines promising research directions in lip-reading, therefore offering structured guidance for the continued development of multilingual visual speech recognition systems.