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相关概念视频

Non-Verbal Cues01:29

Non-Verbal Cues

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Non-verbal communication extends beyond gestures and facial expressions to include vocal elements known as paralanguage. Paralanguage consists of non-verbal vocal cues such as pitch, loudness, speech rate, pauses, and non-verbal vocalizations like laughter, sighs, and moans. These elements not only accompany speech but also provide critical emotional and contextual information.The Role of Paralanguage in CommunicationParalanguage adds depth to spoken language by conveying emotions and...
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通过深度自动口唇阅读增强视觉语音感知:一个系统的审查.

Griffani Megiyanto Rahmatullah1, Shanq-Jang Ruan2, I Wayan Wiprayoga Wisesa3

  • 1Department of Electronic and Computer Engineering, National Taiwan University of Science and Technology, No. 43, Sec. 4, Keelung Rd, Taipei, 10607, Taiwan; Electrical Electronic Engineering, Politeknik Negeri Bandung, Jl. Gegerkalong Hilir, Ciwaruga, Kec. Parongpong, Bandung, 40012, West Java, Indonesia.

Computers in biology and medicine
|March 29, 2025
PubMed
概括
此摘要是机器生成的。

人工智能 (AI) 和深度学习正在推进自动唇读技术,以改善听力障碍患者的沟通. 虽然最先进的模型显示出希望,但数据限制和环境因素等挑战需要进一步研究.

关键词:
深度学习是一种深度学习.嘴唇阅读是什么意思神经网络的神经网络的神经网络普里斯马是什么意思 普里斯马是什么意思系统的文献审查 系统的文献审查

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科学领域:

  • 计算机科学 计算机科学
  • 人工智能的人工智能
  • 人与计算机的交互

背景情况:

  • 听力损失带来了重要的沟通障碍.
  • 传统的沟通辅助工具,如手语和手动唇读有其局限性.
  • 由人工智能驱动的软件提供了新兴的,更有效的通信解决方案.

研究的目的:

  • 进行关于自动唇读研究趋势的系统文献综述 (SLR).
  • 分析自动唇读系统中的进步,数据集,任务和架构.
  • 确定该领域的挑战和未来方向.

主要方法:

  • 按照PRISMA协议进行的系统文献审查 (SLR).
  • 分析了2014年至2024年中期发表的114篇研究文章.
  • 趋势,数据集,任务类别,方法和架构的总结.

主要成果:

  • 深度学习模型在自动唇读中展示了最先进的性能.
  • 在各种技术和先进的深度学习架构方面取得了重大进展.
  • 关键的挑战包括数据不足,环境变化和语言多样性.

结论:

  • 自动读唇技术,特别是使用深度学习的技术,正在迅速发展.
  • 解决数据稀缺性,环境稳定性和语言包容性对于未来的发展至关重要.
  • 人工智能驱动的口唇阅读具有很大的潜力,可以增强听力障碍者的沟通.