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@article{190752,
        author = {ADITHI H S and PRATHEEKSHA K N and Aaliya Waseem},
        title = {Learning Speech from Silence: An AI-Driven Framework for Silent Communication},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {8},
        pages = {4354-4360},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=190752},
        abstract = {Silent Speech Recognition (SSR) aims to infer spoken content without relying on audible sound, enabling communication through purely visual or non-acoustic cues. This paper introduces an AI-driven framework for silent communication that learns to decode speech directly from lip movement dynamics. The proposed system employs a deep learning architecture that integrates Convolutional Neural Networks for capturing fine-grained spatial characteristics of articulatory motion and Recurrent Neural Networks for modeling temporal dependencies across successive video frames. By leveraging large-scale visual speech datasets, the framework learns robust visual–linguistic representations capable of mapping silent lip gestures to textual speech outputs. The model is evaluated on both isolated word recognition and continuous visual speech sequences, demonstrating encouraging recognition performance across varied speaking conditions. The results indicate that deep neural models can effectively translate silent articulatory patterns into meaningful language constructs, offering a viable pathway for assistive communication, privacy-preserving interaction, and speech-enabled systems in acoustically constrained environments. This work highlights the potential of AI-based lip-reading as a foundational technology for next-generation silent communication interfaces.},
        keywords = {Silent Speech Recognition; Lip Reading; Deep Learning; Visual Speech Recognition; Assistive Communication; Human–Computer Interaction},
        month = {January},
        }