Hybrid Attention Mechanisms in 3D CNN for Noise-Resilient Lip Reading in Complex Environments

This paper presents a novel lipreading approach implemented through a web application that automatically generates subtitles for videos where the speaker's mouth movements are visible. The proposed solution leverages a deep learning architecture combining 3D convolutional neural networks (CNN) with bidirectional Long Short-Term Memory (LSTM) units to accurately predict sentences based solely on visual input. A thorough review of existing lipreading techniques over the past decade is provided to contextualize the advancements introduced in this work. The primary goal is to improve the accuracy and usability of lipreading technologies, with a focus on real-world applications. This study contributes to the ongoing progress in the field, offering a robust, scalable solution for enhancing automated visual speech recognition systems.