视觉语音分析的深度学习:一项调查
概括
深度学习显著推进了视觉语音分析,用于安全和娱乐等应用. 本综述涵盖了最近的深度学习方法,挑战和视觉语音识别和生成的未来方向.
科学领域:
- 计算机视觉 计算机视觉
- 人工智能的人工智能
- 语音处理 语音处理
背景情况:
- 视觉语音分析,利用唇部运动和面部线索,在各种领域越来越重要.
- 深度学习技术彻底改变了视觉语音学习,特别是在识别和生成方面.
- 最近的进展需要对这个领域的深度学习应用进行整合的概述.
研究的目的:
- 为视觉语音分析应用的深度学习方法提供全面的审查.
- 巩固最近的进展并确定关键挑战和未来的研究方向.
- 作为视觉语言学习研究人员的资源.
主要方法:
- 对基于深度学习的视觉语音分析方法进行系统审查.
- 基于基本问题和任务 (例如,识别,生成) 的方法的分类.
- 对基准数据集,绩效指标和最新结果的分析.
主要成果:
- 识别了许多深度学习技术,以增强视觉语音识别和生成.
- 概述当前的挑战,包括数据的变化和现实世界的适用性.
- 各种方法的基准数据集和性能基准的摘要.
结论:
- 深度学习已经大大提高了视觉语音分析能力.
- 需要进一步的研究来解决现有的差距,并探索新的应用.
- 本次综述强调了该领域有前途的未来研究方向.
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