35.1424, Books: Digital Tools for Sign Language Research: Fragkiadakis (2024)

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LINGUIST List: Vol-35-1424. Thu May 09 2024. ISSN: 1069 - 4875.

Subject: 35.1424, Books: Digital Tools for Sign Language Research: Fragkiadakis (2024)

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Date: 17-Apr-2024
From: Tessa Arneri [lotdissertations-fgw at uva.nl]
Subject: Digital Tools for Sign Language Research: Fragkiadakis (2024)


Title: Digital Tools for Sign Language Research
Subtitle: Towards Recognition and Comparison of Lexical Signs
Series Title: LOT Dissertation Series
Publication Year: 2024
Publisher: Netherlands Graduate School of Linguistics / Landelijke
(LOT)
                http://www.lotpublications.nl/
Book URL: https://dx.medra.org/10.48273/LOT0666

Author: Manolis Fragkiadakis
Paperback: ISBN: 978-94-6093-451-3 Pages: 165 Price: Europe EURO 32
Abstract:

Contrary to common belief, sign languages are not universal but are
unique to specific communities and cultures. They develop organically
through interactions among deaf individuals and are not derivatives of
spoken languages. Each sign language boasts its own distinct grammar,
lexicon, and cultural nuances. Variations and dialects of sign
languages can even be observed within a single country, reflecting the
rich and diverse modes of communication within the deaf community.

Despite this linguistic richness, deaf individuals often face pressure
to adopt spoken language methods, such as lipreading or text-based
communication. This bias toward spoken languages is further reinforced
by the noticeable absence of signed languages in current linguistic
technologies, underscoring the need for greater inclusion and
recognition in language research and technological development.

Acknowledging the structural and cultural significance of sign
languages, this dissertation seeks to bridge the technological gap by
leveraging machine and deep learning methodologies to enhance sign
language processing and recognition. Spanning six chapters, it
introduces various innovative approaches: methods for annotating sign
sequences from videos, search systems for sign language dictionaries
that operate via webcam input, and systems designed to rank sign
suggestions. Additionally, it delves into the development of tools for
visualizing and comparing variations in sign languages, enriching the
resources available for linguistic research.

Linguistic Field(s): Computational Linguistics

Language Family(ies): Sign Language

Written In: English (eng)



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