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The paper builds on LipNet with a standardized mouth-region preprocessing pipeline, a rotational validation scheme for unseen speakers, and a more flexible similarity metric. On the GRID dataset it reports 95.0% character accuracy and a 13.1% WER, which it presents as an improvement over the LipNet baseline and competitive with 3D-ResNet + Bi-GRU + MixUp. The results are benchmark-specific, though, and GRID is a constrained, controlled dataset, so readers should treat the gains as evidence about evaluation methods and generalization strategies rather than proof of real-world lip-reading performance.
LipLingo: CNN Model for Lip Reading
arXiv:2611.02776v1 Announce Type: new 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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