Exploring Sign Language Detection on Smartphones: A Systematic Review of Machine and Deep Learning Approaches

In this modern era of technology, most of the accessibility issues are handled with the help of smart devices and cutting-edge gadgets. Smartphones play a crucial role in addressing various accessibility challenges, including voice recognition, sign language detection and interpretation, navigation...

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Veröffentlicht in:Advances in human-computer interaction Jg. 2024; S. 1 - 36
Hauptverfasser: Alam, Iftikhar, Hameed, Abdul, Ziar, Riaz Ahmad
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York Hindawi 11.03.2024
John Wiley & Sons, Inc
Wiley
Schlagworte:
Accessibility
Accuracy
Analysis
Cable television broadcasting industry
Cameras
Communication
Computational linguistics
Cutting equipment
Deep learning
Disabilities
Disability
Disorders
Electronic devices
Electronics in navigation
Hearing
Hearing loss
Language
Language processing
Learning algorithms
Literature reviews
Machine learning
Natural language interfaces
Navigation systems
Neural networks
Physically disabled persons
Sensors
Sign language
Smart phones
Smartphones
Speech
Speech disorders
Support vector machines
Systematic review
Translating and interpreting
Trends
Voice communication
Voice recognition
United States
Pakistan
India
ISSN:1687-5893, 1687-5907
Online-Zugang:Volltext
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Abstract In this modern era of technology, most of the accessibility issues are handled with the help of smart devices and cutting-edge gadgets. Smartphones play a crucial role in addressing various accessibility challenges, including voice recognition, sign language detection and interpretation, navigation systems, speech-to-text conversion, and vice versa, among others. They are computationally powerful enough to handle and run numerous machine and deep learning applications. Among various accessibility challenges, speech disorders represent a disability where individuals struggle to communicate verbally. Similarly, hearing loss is a disability that impairs an individual’s ability to hear, necessitating reliance on gestures for communication. A significant challenge encountered by people with speech disorders, hearing loss, or both is their inability to effectively convey or receive messages from others. Hence, these individuals heavily depend on the sign language (a gesture-based communication) method, typically involving hand movements and expressions. To the best of our knowledge, there are currently no comprehensive review and/or survey articles available that cover the literature on speech disabilities and sign language detection and interpretation via smartphones utilizing machine learning and/or deep learning approaches. This study fills the gap in the literature by analyzing research publications on speech disabilities, published from 2012 to July 2023. A rigorous search and standard strategy for formulating the literature along with a well-defined theoretical framework for results and findings have been used. The paper has implications for practitioners and researchers working in accessibilities in general and smart/intelligent gadgets and applications for speech-disabled people in specific.
AbstractList In this modern era of technology, most of the accessibility issues are handled with the help of smart devices and cutting-edge gadgets. Smartphones play a crucial role in addressing various accessibility challenges, including voice recognition, sign language detection and interpretation, navigation systems, speech-to-text conversion, and vice versa, among others. They are computationally powerful enough to handle and run numerous machine and deep learning applications. Among various accessibility challenges, speech disorders represent a disability where individuals struggle to communicate verbally. Similarly, hearing loss is a disability that impairs an individual’s ability to hear, necessitating reliance on gestures for communication. A significant challenge encountered by people with speech disorders, hearing loss, or both is their inability to effectively convey or receive messages from others. Hence, these individuals heavily depend on the sign language (a gesture-based communication) method, typically involving hand movements and expressions. To the best of our knowledge, there are currently no comprehensive review and/or survey articles available that cover the literature on speech disabilities and sign language detection and interpretation via smartphones utilizing machine learning and/or deep learning approaches. This study fills the gap in the literature by analyzing research publications on speech disabilities, published from 2012 to July 2023. A rigorous search and standard strategy for formulating the literature along with a well-defined theoretical framework for results and findings have been used. The paper has implications for practitioners and researchers working in accessibilities in general and smart/intelligent gadgets and applications for speech-disabled people in specific.
Audience Academic
Author Ziar, Riaz Ahmad
Hameed, Abdul
Alam, Iftikhar
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  organization: Department of Computer ScienceKardan UniversityKabul 1001Afghanistankardan.edu.af
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Snippet In this modern era of technology, most of the accessibility issues are handled with the help of smart devices and cutting-edge gadgets. Smartphones play a...
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SubjectTerms Accessibility
Accuracy
Analysis
Cable television broadcasting industry
Cameras
Communication
Computational linguistics
Cutting equipment
Deep learning
Disabilities
Disability
Disorders
Electronic devices
Electronics in navigation
Hearing
Hearing loss
Language
Language processing
Learning algorithms
Literature reviews
Machine learning
Natural language interfaces
Navigation systems
Neural networks
Physically disabled persons
Sensors
Sign language
Smart phones
Smartphones
Speech
Speech disorders
Support vector machines
Systematic review
Translating and interpreting
Trends
Voice communication
Voice recognition
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Title Exploring Sign Language Detection on Smartphones: A Systematic Review of Machine and Deep Learning Approaches
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