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 applicationthat automatically generates subtitles for videos where the speaker's mouth movements arevisible. The proposed solution leverages a deep learning architecture combining 3Dconvolutional neural networks (CNN) with bidirectional Long Short-Term Memory (LSTM)units to accurately predict sentences based solely on visual input. A thorough review ofexisting lipreading techniques over the past decade is provided to contextualize theadvancements introduced in this work. The primary goal is to improve the accuracy andusability of lipreading technologies, with a focus on real-world applications. This studycontributes to the ongoing progress in the field, offering a robust, scalable solution forenhancing automated visual speech recognition systems.