Revolutionizing Lip Reading: The Power of Temporal Attention (S)

This paper presents a novel Convolutional Based Temporal Attention (CBTA) module that improves the performance of temporal convolutional networks (TCN) in lipreading tasks without requiring any additional data.Our CBTA method enhances recognition accuracy by focusing attention on relevant frames in the time sequence of a video.The study demonstrates how our strategy achieves groundbreaking sucacess on the Lip Reading in the Wild (LRW) dataset, achieving an accuracy of 92.61%-surpassing contemporary methods by approximately 0.5%.The experiment also indicates the broad utility and effectiveness of the adaptable CBTA.The proposed module substantially boosts Top-1 Accuracy for challenging words, offering a promising direction for overall performance improvement in lip-reading tasks.