Deep Learning for Visual Speech Analysis: A Survey.

Area of Science:

• Computer Vision
• Artificial Intelligence
• Speech Processing

Background:

• Visual speech analysis, leveraging lip movements and facial cues, has growing importance in diverse fields.
• Deep learning techniques have revolutionized visual speech learning, particularly in recognition and generation.
• Recent advancements necessitate a consolidated overview of deep learning applications in this domain.

Purpose of the Study:

• To provide a comprehensive review of deep learning methods applied to visual speech analysis.
• To consolidate recent progress and identify key challenges and future research directions.
• To serve as a resource for researchers in visual speech learning.

Main Methods:

• Systematic review of deep learning-based approaches for visual speech analysis.
• Categorization of methods based on fundamental problems and tasks (e.g., recognition, generation).
• Analysis of benchmark datasets, performance metrics, and state-of-the-art results.

Main Results:

• Identification of numerous deep learning techniques enhancing visual speech recognition and generation.
• Overview of current challenges, including data variability and real-world applicability.
• Summary of benchmark datasets and performance benchmarks for various methods.

Conclusions:

• Deep learning has substantially improved visual speech analysis capabilities.
• Further research is needed to address existing gaps and explore novel applications.
• This review highlights promising future research directions in the field.