Deep Learning for Visual Speech Analysis: A Survey
It provides a comprehensive overview for researchers in AI and computer vision, but is incremental as it synthesizes existing work without introducing new methods.
This survey reviews recent progress in deep learning methods for visual speech analysis, covering problems like automatic visual speech recognition and generation, and includes benchmark datasets and state-of-the-art performance metrics.
Visual speech, referring to the visual domain of speech, has attracted increasing attention due to its wide applications, such as public security, medical treatment, military defense, and film entertainment. As a powerful AI strategy, deep learning techniques have extensively promoted the development of visual speech learning. Over the past five years, numerous deep learning based methods have been proposed to address various problems in this area, especially automatic visual speech recognition and generation. To push forward future research on visual speech, this paper aims to present a comprehensive review of recent progress in deep learning methods on visual speech analysis. We cover different aspects of visual speech, including fundamental problems, challenges, benchmark datasets, a taxonomy of existing methods, and state-of-the-art performance. Besides, we also identify gaps in current research and discuss inspiring future research directions.