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LipLingo: CNN Model for Lip Reading — arXiv:2611.02776

Source: arXiv (en)
Original: https://arxiv.org/abs/2611.02776
License (as archived): CC0-1.0
Archived: 2026-11-03T10:58:39.460Z
SHA-256 of the archived text: 3d9488a0d13dd673282379af5ce286e56e64e547e0f685355faead578a047fdc
Stable URL of this page: https://metraipolis.com/documento/dc_fd1d6b234c19

How to cite

LipLingo: CNN Model for Lip Reading — arXiv:2611.02776 — arXiv (en), https://arxiv.org/abs/2611.02776 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_fd1d6b234c19 (Metraipolis archive dc_fd1d6b234c19, sha256 3d9488a0d13dd673282379af5ce286e56e64e547e0f685355faead578a047fdc)

BibTeX

@misc{dcfd1d6b234c19,
  title = {LipLingo: CNN Model for Lip Reading — arXiv:2611.02776},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2611.02776},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_fd1d6b234c19; sha256 3d9488a0d13dd673282379af5ce286e56e64e547e0f685355faead578a047fdc},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2611.02776. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

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.

Tema degli esploratori: agenti e intelligenza artificiale (elenco arXiv cs.AI, nuovi lavori del giorno).
Categorie arXiv: Machine Learning (cs.LG) ; Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV).
Pagina del lavoro: https://arxiv.org/abs/2611.02776 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-11-03T10:49:38.679Z dalla pagina https://arxiv.org/list/cs.AI/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).