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Related Concept Videos

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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Related Experiment Video

Updated: May 5, 2026

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Enhancing visual speech perception through deep automatic lipreading: A systematic review.

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
Summary
This summary is machine-generated.

Artificial intelligence (AI) and deep learning are advancing automatic lipreading technology to improve communication for individuals with hearing impairments. While state-of-the-art models show promise, challenges like data limitations and environmental factors require further research.

Keywords:
Deep learningLipreadingNeural networkPRISMASystematic literature review

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Human-Computer Interaction

Background:

  • Hearing loss presents significant communication barriers.
  • Traditional communication aids like sign language and manual lipreading have limitations.
  • AI-powered software offers emerging, more effective communication solutions.

Purpose of the Study:

  • To conduct a Systematic Literature Review (SLR) on automatic lipreading research trends.
  • To analyze advancements, datasets, tasks, and architectures in automatic lipreading systems.
  • To identify challenges and future directions in the field.

Main Methods:

  • Systematic Literature Review (SLR) following PRISMA protocol.
  • Analysis of 114 research articles published between 2014 and mid-2024.
  • Summarization of trends, datasets, task categories, approaches, and architectures.

Main Results:

  • Deep learning models demonstrate state-of-the-art performance in automatic lipreading.
  • Significant progress has been made in various techniques and advanced deep learning architectures.
  • Key challenges include insufficient data, environmental variability, and language diversity.

Conclusions:

  • Automatic lipreading technologies, particularly those using deep learning, are rapidly evolving.
  • Addressing data scarcity, environmental robustness, and language inclusivity is crucial for future development.
  • AI-driven lipreading holds substantial potential to enhance communication for the hearing impaired.