Sign Language Processing
From Gesture to Meaning
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Beschreibung
In a world where communication is key to human connection, understanding, and learning from one another, the book investigates the rich and intricate world of sign languages, highlighting the fascinating complexities of visual-spatial languages and their unique role in bridging the gap between hearing and deaf communities through information and communication technology.
The book takes a journey through the evolution of sign language processing tools while exploring the cutting-edge techniques used to decipher, analyze, and process them, from the fundamentals of sign language structure and the nuances of non-manual signals to the latest developments in computational linguistics (corpora design, annotation tools, and notation systems), sign language recognition, and machine/deep learning applications.
With a mission to reveal the silent language of expression, the author provides a captivating and thought-provoking look into a world often overlooked yet teeming with life and meaning. Offering a comprehensive and engaging overview of the current state of sign language research as well as its prospects, this monograph is an introduction resource for computer scientists, linguistics, educators, academics and sign language interpreters alike.
This monograph is an invitation to discover the incredible potential of sign languages to transform human communication, promote inclusivity, and shape the future of technology. The readers will gain a deeper appreciation of the beauty and complexity of sign languages through technology, and they will be inspired to embrace the power of visual communication in our increasingly diverse and interconnected world.
In a world where communication is key to human connection, understanding, and learning from one another, the book investigates the rich and intricate world of sign languages, highlighting the fascinating complexities of visual-spatial languages and their unique role in bridging the gap between hearing and deaf communities through information and communication technology.
The book takes a journey through the evolution of sign language processing tools while exploring the cutting-edge techniques used to decipher, analyze, and process them, from the fundamentals of sign language structure and the nuances of non-manual signals to the latest developments in computational linguistics (corpora design, annotation tools, and notation systems), sign language recognition, and machine/deep learning applications.
With a mission to reveal the silent language of expression, the author provides a captivating and thought-provoking look into a world often overlooked yet teeming with life and meaning. Offering a comprehensive and engaging overview of the current state of sign language research as well as its prospects, this monograph is an introduction resource for computer scientists, linguistics, educators, academics and sign language interpreters alike.
This monograph is an invitation to discover the incredible potential of sign languages to transform human communication, promote inclusivity, and shape the future of technology. The readers will gain a deeper appreciation of the beauty and complexity of sign languages through technology, and they will be inspired to embrace the power of visual communication in our increasingly diverse and interconnected world.
Dives into the complexities and innovations driving sign language avatar technologies and virtual interpreters Explores the role of AI, computer vision, and machine learning in sign language recognition, envisioning its future Discusses ethical and societal challenges in sign language processing, from data collection to technology deployment
Autor*in
Achraf Othman
Themen in »Sign Language Processing«
Convolutional Neural Networks Cross-Cultural Communication Deep Learning Endangered Sign Languages Sign Language Processing Machine Learning Machine Translation Natural Language Processing Recurrent Neural Networks Sign Language Annotation Sign Language Recognition Sign Language Translation Support Vector Machines Transfer Learning