Year: 2024
Type: conference-paper
Abstract: 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.
Authors: Jianfeng Shi, Qifei Zhang, Furen Bai, Wenjuan Li
Institutions: Zhejiang University, Hangzhou Normal University
Language: English
Topic: Speech and Audio Processing
Subfield: Signal Processing
Field: Computer Science
Domain: Physical Sciences
Open Access status: bronze
OpenAlex ID: W4406255317