LipLingo improves lip reading accuracy with CNN and preprocessing
LipLingo: CNN Model for Lip Reading
Machine LearningArtificial IntelligenceComputer Vision and Pattern Recognition
Summary
Lip reading helps turn silent lip movements into words, useful for hearing-impaired communication and security. The authors created LipLingo, a system that looks carefully at video of lips using special processing and a type of deep learning to better understand what is being said. They tested it on a standard dataset and found it works more accurately than older methods, especially when different people speak. This could help make lip reading technology work better in real-world situations.
What this means in practice
- •For assistive technology developers: Improve speech recognition tools for hearing-impaired users by incorporating more robust lip-reading models that handle different speakers and visually similar sounds.
- •For security system designers: Enhance biometric authentication methods by integrating lip reading models that accurately interpret lip movements to verify identities.
Authors
Mahmoud S. Mayaleh, Liban Ali Achkir, Nijat Mehdiyev, Ahmet Ozdil
Abstract
Lip reading plays a crucial role in applications such as speech recognition, assistive technologies for the hearing-impaired, and biometric authentication. However, performance often degrades when the speaker varies and when certain speech sounds are visually similar. To address these challenges, we propose LipLingo, a robust lip-reading system based on the LipNet architecture, enhanced with a standardized preprocessing pipeline for consistent mouth-region representation, a rotational validation strategy for improved generalization to unseen speakers, and a flexible similarity evaluation for more reliable sentence-level assessment. The model architecture combines 3D convolutional layers for spatio-temporal feature extraction and bidirectional recurrent layers for sequence modeling, optimized using the Adam optimizer and Connectionist Temporal Classification (CTC) loss. On the GRID dataset, LipLingo achieved 95.0\% character-level accuracy (CER 4.99\%) and 87.2\% sentence-level accuracy. At the corpus level, the system obtained a (WER 13.1\%), outperforming the baseline LipNet and demonstrating competitive performance relative to recent 3D-ResNet + Bi-GRU + MixUp approaches. These results provide insights into strategies that enhance lip-reading robustness and highlight directions for future work toward real-world deployment.