# LipLingo: CNN Model for Lip Reading

**Authors:** Mahmoud S. Mayaleh, Liban Ali Achkir, Nijat Mehdiyev, Ahmet Ozdil  
**Lab:** Ostim Technical University · **Published:** 2 Nov 2026  
**Links:** [arXiv](https://arxiv.org/abs/2611.02776) · [PDF](https://arxiv.org/pdf/2611.02776) · [This page](https://omen-bit.github.io/ai-paper-daily/paper/2611.02776.html)

## 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.

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