| Abstract |
Sign languages are natural languages native to the Deaf or Hard-of-Hearing
communities. They are not mere transpositions of an oral language, but have
their own origin and evolution, as well as their own lexicon, grammar, and
idiosyncrasy.
This makes them a legitimate object of study for the fields of Computational
Linguistics and Natural Language Processing. This thesis describes research
framed within this study, with an emphasis on Spanish Sign Language and its
SignWriting. The doctoral research conducted is articulated in six
publications, developed within the framework of a competitively funded
research project led by the author, “Visualizing SignWriting” (VisSE).
An unifying discussion is included that establishes and justifies the
objectives of the research. A critical analysis of the current state of the
question is also conducted, considering essential aspects for sign language
research, such as methods for their automatic recognition, their grammar and
structure, and their written representation. It is precisely this last topic
that motivates the main technical objective of the thesis: improving the
automatic processing of SignWriting.
In this context, an original corpus of SignWriting samples has been
collected, for which a new and complex annotation scheme based on its
linguistics has been developed. Based on this corpus, Artificial
Intelligence algorithms, specifically deep learning neural networks, have
been trained to recognize and interpret SignWriting. The effectiveness of
these algorithms has been enhanced by creating an expert system that
combines them with logical rules, derived from the theoretical analysis
performed. These developments have been made possible thanks to a Python
library built for this research, Quevedo, which allows managing data corpora
as well as visualizing them, annotating them and processing them. A web
application has also been created in order to transfer the research results
to society. This application converts SignWriting into textual explanations
and includes a three-dimensional model of the hand.
Additionally, this thesis has not only contributed technical advances in the
processing of SignWriting but has also generated in-depth knowledge about
Spanish Sign Language, materialized in an underlying model and in various
publications and computational applications not included in the compendium
but referenced in this document.
The software and data generated during the research have been released under
open source licenses, thus allowing future researchers to continue advancing
in this area without having to start from scratch.
|
| Citation |
@phdthesis{SevillaTratamientoLSE2023,
author = {Sevilla, Antonio F. G.},
title = {Tratamiento Computacional de {L}engua de
{S}ignos {E}spañola y {S}igno{E}scritura},
school = {Universidad Complutense de Madrid,
Facultad de Informática},
year = {2023},
month = {12},
note = {\url{https://garciasevilla.com/tesis}},
}
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