Lexical Resources for Sign Language Synthesis
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An Animated Avatar to Interpret Signwriting Transcription
An Animated Avatar to Interpret SignWriting Transcription Yosra Bouzid Mohamed Jemni Research Laboratory of Technologies of Information and Research Laboratory of Technologies of Information and Communication & Electrical Engineering (LaTICE) Communication & Electrical Engineering (LaTICE) ESSTT, University of Tunis ESSTT, University of Tunis [email protected] [email protected] Abstract—People with hearing disability often face multiple To address this issue, different methods and technologies barriers when attempting to interact with hearing society. geared towards deaf communication have been suggested over The lower proficiency in reading, writing and understanding the the last decades. For instance, the use of sign language spoken language may be one of the most important reasons. interpreters is one of the most common ways to ensure a Certainly, if there were a commonly accepted notation system for successful communication between deaf and hearing persons in sign languages, signed information could be provided in written direct interaction. But according to a WFD survey in 2009, 13 form, but such a system does not exist yet. SignWriting seems at countries out of 93 do not have any sign language interpreters, present the best solution to the problem of SL representation as it and in many of those countries where there are interpreters, was intended as a practical writing system for everyday there are serious problems in finding qualified candidates. communication, but requires mastery of a set of conventions The digital technologies, particularly video and avatar-based different from those of the other transcriptions systems to become a proficient reader or writer. To provide additional systems, contribute, in turn, in bridging the communication support for deaf signers to learn and use such notation, we gap. -
The Signing Space for the Synthesis of Directional Verbs in NGT
The signing space for the synthesis of directional verbs in NGT Shani E. Mende-Gillings Layout: typeset by the author using LATEX. Cover illustration: Unknown artist The signing space for the synthesis of directional verbs in NGT Shani E. Mende-Gillings 11319836 Bachelor thesis Credits: 18 EC Bachelor Kunstmatige Intelligentie University of Amsterdam Faculty of Science Science Park 904 1098 XH Amsterdam Supervisor dr. F. Roelofsen Institute for Logic, Language and Computation Faculty of Science University of Amsterdam Science Park 107 1098 XG Amsterdam 26 June 2020 Acknowledgements I would like to thank my supervisor Floris Roelofsen for his guidance during this project. I am also very grateful to John Glauert and Richard Kennaway for proving valuable insight into SiGML and the JASigning software. Special thanks go to Inge Zwitserlood, Onno Crasborn and Johan Ros for providing the database of encoded NGT signs, without which this project would not have been possible. Last, but not least, I would also like to thank Marijke Scheffener for providing evaluation material and valuable feedback on the program. Preface This paper describes an individual contribution to a larger project executed in close collaboration with [1] and [2]. The first parts of these three papers describe the overall project and were jointly written, making them largely identical. Section 1.2 describes the global research question, and sections 2 and 3 provide the- oretical context and set up a hypothesis. Section 5.1.1 shows the overall program created, including the components of the other projects, and section 5.2.1 evaluates the result. These previously mentioned chap- ters and sections have been written in joint collaboration with [1] and [2] An introduction to and motivation for the overall project are provided in section 1. -
Sign Classification in Sign Language Corpora with Deep Neural Networks
Sign Classification in Sign Language Corpora with Deep Neural Networks Lionel Pigou, Mieke Van Herreweghe, Joni Dambre Ghent University flionel.pigou, mieke.vanherreweghe, [email protected] Abstract Automatic and unconstrained sign language recognition (SLR) in image sequences remains a challenging problem. The variety of signers, backgrounds, sign executions and signer positions makes the development of SLR systems very challenging. Current methods try to alleviate this complexity by extracting engineered features to detect hand shapes, hand trajectories and facial expressions as an intermediate step for SLR. Our goal is to approach SLR based on feature learning rather than feature engineering. We tackle SLR using the recent advances in the domain of deep learning with deep neural networks. The problem is approached by classifying isolated signs from the Corpus VGT (Flemish Sign Language Corpus) and the Corpus NGT (Dutch Sign Language Corpus). Furthermore, we investigate cross-domain feature learning to boost the performance to cope with the fewer Corpus VGT annotations. Keywords: sign language recognition, deep learning, neural networks 1. Introduction In previous work (Pigou et al., 2015), we showed that deep neural networks are very successful for gesture recognition SLR systems have many different use cases: corpus anno- and gesture spotting in spatiotemporal data. Our developed tation, in hospitals, as a personal sign language learning system is able to recognize 20 different Italian gestures (i.e., assistant or translating daily conversations between signers emblems). We achieved a classification accuracy of 97.23% and non-signers to name a few. Unfortunately, unconstrained in the Chalearn 2014 Looking At People gesture spotting SLR remains a big challenge. -
Gesture and Sign Language Recognition with Temporal Residual Networks
Gesture and Sign Language Recognition with Temporal Residual Networks Lionel Pigou, Mieke Van Herreweghe and Joni Dambre Ghent University {lionel.pigou, mieke.vanherreweghe, joni.dambre}@ugent.be Abstract SLR using recurrent CNNs [6]. A task that is closely related to SLR is gesture recog- Gesture and sign language recognition in a continuous nition. Deep neural networks have proven to be success- video stream is a challenging task, especially with a large ful for this problem, given a small vocabulary (20 gestures) vocabulary. In this work, we approach this as a framewise [22, 30] and/or with a multi-modal approach [24, 18]. classification problem. We tackle it using temporal con- In this work, we investigate large vocabulary gesture volutions and recent advances in the deep learning field recognition and SLR using deep neural networks with up- like residual networks, batch normalization and exponen- to-date architectures, regularization techniques and training tial linear units (ELUs). The models are evaluated on methods. To achieve this, we approach the problem as a three different datasets: the Dutch Sign Language Corpus continuous framewise classification task, where the tempo- (Corpus NGT), the Flemish Sign Language Corpus (Cor- ral locations of gestures and signs are not given during eval- pus VGT) and the ChaLearn LAP RGB-D Continuous Ges- uation. The models are tested on the Dutch Sign Language ture Dataset (ConGD). We achieve a 73.5% top-10 accu- Corpus (Corpus NGT) [4, 5], the Flemish Sign Language racy for 100 signs with the Corpus NGT, 56.4% with the Corpus (Corpus VGT) [27] and the ChaLearn LAP RGB-D Corpus VGT and a mean Jaccard index of 0.316 with the Continuous Gesture Dataset (ConGD) [28]. -
A Lexical Comparison of South African Sign Language and Potential Lexifier Languages
A lexical comparison of South African Sign Language and potential lexifier languages by Andries van Niekerk Thesis presented in partial fulfilment of the requirements for the degree Masters in General Linguistics at the University of Stellenbosch Supervisors: Dr Kate Huddlestone & Prof Anne Baker Faculty of Arts and Social Sciences Department of General Linguistics March, 2020 Stellenbosch University https://scholar.sun.ac.za DECLARATION By submitting this thesis electronically, I declare that the entirety of the work contained therein is my own, original work, that I am the sole author thereof (save to the extent explicitly otherwise stated), that reproduction and publication thereof by Stellenbosch University will not infringe any third party rights and that I have not previously in its entirety or in part submitted it for obtaining any qualification. Andries van Niekerk March 2020 Copyright © 2020 University of Stellenbosch All rights reserved 1 Stellenbosch University https://scholar.sun.ac.za ABSTRACT South Africa’s history of segregation was a large contributing factor for lexical variation in South African Sign Language (SASL) to come about. Foreign sign languages certainly had a presence in the history of deaf education; however, the degree of influence foreign sign languages has on SASL today is what this study has aimed to determine. There have been very limited studies on the presence of loan signs in SASL and none have included extensive variation. This study investigates signs from 20 different schools for the deaf and compares them with signs from six other sign languages and the Paget Gorman Sign System (PGSS). A list of lemmas was created that included the commonly used list of lemmas from Woodward (2003). -
The Signed Languages of Indonesia: an Enigma
DigitalResources Electronic Survey Report 2014-005 ® The Signed Languages of Indonesia: An Enigma Hope M. Hurlbut The Signed Languages of Indonesia: An Enigma Hope M. Hurlbut SIL International® 2014 SIL Electronic Survey Report 2014-005, April 2014 © 2014 SIL International® All rights reserved 1 2 Abstract In 2003–2005, SIL International undertook a lexicostatistical survey of the signed languages of Indonesia. Wordlists and stories were collected from each of the nineteen states where one or more schools for the Deaf were run privately or by the government. The wordlists were video recorded and transcribed by hand using the SignWriting orthography. The results of the wordlist comparisons point out the need for intelligibility testing between users of the various varieties of Indonesian Sign Language. Intelligibility testing should be carried out sometime in at least eleven of the nineteen states where the similarity between the signs in the list is low. This paper focuses on the results of the lexicostatistical survey. There are at least two signed languages in use in Indonesia, Indonesian Sign Language and Bengkala Sign Language. Bengkala Sign Language is an isolect found in northern Bali in the village of Bengkala where there is a high proportion of Deaf among the inhabitants. It has been called Bali Sign Language in the past, but since it seems to be more or less confined to the village of Bengkala, it seems better to call it Bengkala Sign Language. The rest of the Deaf on the island use a form of Indonesian Sign Language. At the time of the survey there were two Deaf youth from Bengkala going to school in the Deaf school (or a Deaf class) in Singaraja which is about 17 kilometers from Bengkala Village. -
Typology of Signed Languages: Differentiation Through Kinship Terminology Erin Wilkinson
View metadata, citation and similar papers at core.ac.uk brought to you by CORE provided by University of New Mexico University of New Mexico UNM Digital Repository Linguistics ETDs Electronic Theses and Dissertations 7-1-2009 Typology of Signed Languages: Differentiation through Kinship Terminology Erin Wilkinson Follow this and additional works at: https://digitalrepository.unm.edu/ling_etds Recommended Citation Wilkinson, Erin. "Typology of Signed Languages: Differentiation through Kinship Terminology." (2009). https://digitalrepository.unm.edu/ling_etds/40 This Dissertation is brought to you for free and open access by the Electronic Theses and Dissertations at UNM Digital Repository. It has been accepted for inclusion in Linguistics ETDs by an authorized administrator of UNM Digital Repository. For more information, please contact [email protected]. TYPOLOGY OF SIGNED LANGUAGES: DIFFERENTIATION THROUGH KINSHIP TERMINOLOGY BY ERIN LAINE WILKINSON B.A., Language Studies, Wellesley College, 1999 M.A., Linguistics, Gallaudet University, 2001 DISSERTATION Submitted in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy Linguistics The University of New Mexico Albuquerque, New Mexico August, 2009 ©2009, Erin Laine Wilkinson ALL RIGHTS RESERVED iii DEDICATION To my mother iv ACKNOWLEDGMENTS Many thanks to Barbara Pennacchi for kick starting me on my dissertation by giving me a room at her house, cooking me dinner, and making Italian coffee in Rome during November 2007. Your endless support, patience, and thoughtful discussions are gratefully taken into my heart, and I truly appreciate what you have done for me. I heartily acknowledge Dr. William Croft, my advisor, for continuing to encourage me through the long number of months writing and rewriting these chapters. -
What Corpus-Based Research on Negation in Auslan and PJM Tells Us About Building and Using Sign Language Corpora
What Corpus-Based Research on Negation in Auslan and PJM Tells Us About Building and Using Sign Language Corpora Anna Kuder1, Joanna Filipczak1, Piotr Mostowski1, Paweł Rutkowski1, Trevor Johnston2 1Section for Sign Linguistics, Faculty of the Polish Studies, University of Warsaw, Krakowskie Przedmieście 26/28, 00-927 Warsaw, Poland 2Department of Linguistics, Faculty of Human Sciences, Macquarie University, Sydney, NSW, Australia, 2109 {anna.kuder, j.filipczak, piotr.mostowski, p.rutkowski}@uw.edu.pl, [email protected] Abstract In this paper, we would like to discuss our current work on negation in Auslan (Australian Sign Language) and PJM (Polish Sign Language, polski język migowy) as an example of experience in using sign language corpus data for research purposes. We describe how we prepared the data for two detailed empirical studies, given similarities and differences between the Australian and Polish corpus projects. We present our findings on negation in both languages, which turn out to be surprisingly similar. At the same time, what the two corpus studies show seems to be quite different from many previous descriptions of sign language negation found in the literature. Some remarks on how to effectively plan and carry out the annotation process of sign language texts are outlined at the end of the present paper, as they might be helpful to other researchers working on designing a corpus. Our work leads to two main conclusions: (1) in many cases, usage data may not be easily reconciled with intuitions and assumptions about how sign languages function and what their grammatical characteristics are like, (2) in order to obtain representative and reliable data from large-scale corpora one needs to plan and carry out the annotation process very thoroughly. -
Variation in Mouth Actions with Manual Signs (Chapter 2)
PDF hosted at the Radboud Repository of the Radboud University Nijmegen The following full text is a publisher's version. For additional information about this publication click this link. http://hdl.handle.net/2066/133714 Please be advised that this information was generated on 2021-10-01 and may be subject to change. The ubiquity of mouthings in NGT A corpus study Published by LOT phone: +31 30 253 6111 Trans 10 3512 JK Utrecht e-mail: [email protected] The Netherlands http://www.lotschool.nl Cover illustration: (stemmed) word cloud of this dissertation, from tagul.com. ISBN: 978-94-6093-158-1 NUR 616 Copyright © 2014: Richard Bank. All rights reserved. The ubiquity of mouthings in NGT A corpus study Proefschrift ter verkrijging van de graad van doctor aan de Radboud Universiteit Nijmegen op gezag van de rector magnificus prof. dr. Th. L. M. Engelen volgens besluit van het college van decanen in het openbaar te verdedigen op vrijdag 30 januari 2015 om 12.30 uur precies door Richard Bank Geboren op 1 augustus 1969 te Amsterdam Promotor: Prof. dr. Roeland van Hout Copromotor: Dr. Onno Crasborn Manuscriptcommissie: Prof. dr. Pieter Muysken Prof. dr. Jens Heßmann (Hochschule Magdeburg-Stendal, Duitsland) Prof. dr. Bencie Woll (DCAL, University College London, Groot-Brittannië) Table of contents Dankwoord ..................................................................................................................... ix Chapter 1: Introduction ........................................................................................ 1 1.1 Sign languages: a general introduction ..................................................................... 2 1.1.1 A short history of sign languages...................................................................... 3 1.1.2 Research and recognition .................................................................................. 4 1.1.3 The recent history of NGT ................................................................................ 5 1.1.4 Deaf education in the Netherlands and influence of spoken language ......... -
Analysis of Notation Systems for Machine Translation of Sign Languages
Analysis of Notation Systems for Machine Translation of Sign Languages Submitted in partial fulfilment of the requirements of the degree of Bachelor of Science (Honours) of Rhodes University Jessica Jeanne Hutchinson Grahamstown, South Africa November 2012 Abstract Machine translation of sign languages is complicated by the fact that there are few stan- dards for sign languages, both in terms of the actual languages used by signers within regions and dialogue groups, and also in terms of the notations with which sign languages are represented in written form. A standard textual representation of sign languages would aid in optimising the translation process. This area of research still needs to determine the best, most efficient and scalable tech- niques for translation of sign languages. Being a young field of research, there is still great scope for introducing new techniques, or greatly improving on previous techniques, which makes comparing and evaluating the techniques difficult to do. The methods used are factors which contribute to the process of translation and need to be considered in an evaluation of optimising translation systems. This project analyses sign language notation systems; what systems exists, what data is currently available, and which of them might be best suited for machine translation purposes. The question being asked is how using a textual representation of signs could aid machine translation, and which notation would best suit the task. A small corpus of SignWriting data was built and this notation was shown to be the most accessible. The data was cleaned and run through a statistical machine translation system. The results had limitations, but overall are comparable to other translation systems, showing that translation using a notation is possible, but can be greatly improved upon. -
Sign Language Legislation in the European Union 4
Sign Language Legislation in the European Union Mark Wheatley & Annika Pabsch European Union of the Deaf Brussels, Belgium 3 Sign Language Legislation in the European Union All rights reserved. No part of this book may be reproduced or transmitted by any person or entity, including internet search engines or retailers, in any form or by any means, electronic or mechanical, including photocopying, recording, scanning or by any information storage and retrieval system without the prior written permission of the authors. ISBN 978-90-816-3390-1 © European Union of the Deaf, September 2012. Printed at Brussels, Belgium. Design: Churchill’s I/S- www.churchills.dk This publication was sponsored by Significan’t Significan’t is a (Deaf and Sign Language led ) social business that was established in 2003 and its Managing Director, Jeff McWhinney, was the CEO of the British Deaf Association when it secured a verbal recognition of BSL as one of UK official languages by a Minister of the UK Government. SignVideo is committed to delivering the best service and support to its customers. Today SignVideo provides immediate access to high quality video relay service and video interpreters for health, public and voluntary services, transforming access and career prospects for Deaf people in employment and empowering Deaf entrepreneurs in their own businesses. www.signvideo.co.uk 4 Contents Welcome message by EUD President Berglind Stefánsdóttir ..................... 6 Foreword by Dr Ádám Kósa, MEP ................................................................ -
Automated Sign Language Translation: the Role of Artificial Intelligence Now and in the Future
Automated Sign Language Translation: The Role of Artificial Intelligence Now and in the Future Lea Baumgartner,¨ Stephanie Jauss, Johannes Maucher and Gottfried Zimmermann Hochschule der Medien, Nobelstraße 10, 70569 Stuttgart, Germany Keywords: Sign Language Translation, Sign Language Recognition, Sign Language Generation, Artificial Intelligence. Abstract: Sign languages are the primary language of many people worldwide. To overcome communication barriers between the Deaf and the hearing community, artificial intelligence technologies have been employed, aiming to develop systems for automated sign language recognition and generation. Particularities of sign languages have to be considered - though sharing some characteristics of spoken languages - since they differ in others. After providing the linguistic foundations, this paper gives an overview of state-of-the-art machine learning approaches to develop sign language translation systems and outlines the challenges in this area that have yet to be overcome. Obstacles do not only include technological issues but also interdisciplinary considerations of development, evaluation and adoption of such technologies. Finally, opportunities and future visions of automated sign language translation systems are discussed. 1 INTRODUCTION There are, however, differences in how sign lan- guages work in detail, some of which are mentioned A basic understanding of the particularities and no- using the example of American Sign Language (ASL) tation of sign language serves to better understand in the following. Since suffixes to verbs like in spoken the importance and challenges of automated sign lan- languages are not possible, ASL tenses are built by guage translation. We use both the terms deaf and adding words in the beginning or at the end of a sen- Deaf throughout this paper, the first referring solely tence.