Deaf Communities and the Culture of Language
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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. -
An Exploration of the Word Deaf Rachel Fearon (Linguistics)
Diffusion: the UCLan Journal of Undergraduate Research Volume 6 Issue 1 (June 2013) PEJORISED OR AMELIORATED? AN EXPLORATION OF THE WORD DEAF RACHEL FEARON (LINGUISTICS) Abstract: Since the recognition of British Sign Language in 2003 and the denunciation in 2010 of the 1880 decision to ban sign language in deaf education, the Deaf community has taken a positive move forward as a linguistic minority. This study explores the sense relations of the word deaf through a consideration of (a) quantitative data from a questionnaire study, (b) interviews and correspondence with academics in the field of deaf studies. The paper aims to demonstrate how the concept of the term deaf has changed and proposes that the cultural definition of deafness, Deaf (with capital ‘D’), representing a culture and linguistic minority rather than a disability (in essence Deafhood), needs to be included in the dictionary. This addition would serve to ameliorate the word deaf and provide a permanent, mainstream reminder of the positive sense relations of the word d/Deaf. For the purpose of this article, d/Deaf refers to both audiologically and culturally deaf people. Pejorisation is the process in which over time a word acquires negative meaning and amelioration is a process in which a word regains a positive meaning. Keywords: Pejoration, Amelioration, d/Deaf, H/hearing, Deafhood, Social Model, Medical Model, Cultro-linguistic model. Introduction In Milan in 1880, at the International Congress on Education of the Deaf, a pivotal decision was made to introduce an oral education system, in which signing was forbidden. This resulted in a negative semantic prosody of the word deaf, oppressing d/Deaf people and their freedom to communicate with sign language. -
Predictors of Depressive Symptoms in Persons with Deafness and Hearing Loss Jill Friedman, M.S
Predictors of Depression 1 PREDICTORS OF DEPRESSIVE SYMPTOMS IN PERSONS WITH DEAFNESS AND HEARING LOSS A Thesis Submitted to the Faculty of Drexel University by Jill Friedman In partial fulfillment of the Requirements for the degree of Doctor of Philosophy Drexel University April 2008 Predictors of Depression 2 ABSTRACT Predictors of Depressive Symptoms in Persons with Deafness and Hearing Loss Jill Friedman, M.S. Arthur M. Nezu, Ph.D., ABPP To date, there are very few studies that that have assessed predictors of depression in persons with deafness and hearing loss. The present study addressed this fundamental gap in the literature by predicting depressive symptoms with the following two constructs: loneliness and problem-solving coping. Just as loneliness and problem solving are thought to be important in predicting depressive symptoms in the hearing, it was hypothesized that these constructs would also be important predictors of depressive symptoms in the deaf and hard-of-hearing. The literature, while inconsistent, suggests that objective severity of one’s disability, speech discrimination, and the number of years one is deaf or hard-of-hearing, is related to psychopathology. This study included these factors as covariates. One hundred and twenty six women from the Drexel University Department of Otolaryngology completed a series of self-report questionnaires. The results indicated that loneliness was a significant, positive predictor of depressive symptoms. Additionally, the results indicated that problem solving was a significant, inverse predictor of depressive symptoms. A series of correlations designed to deconstruct the relationship between problem-solving and depression revealed a significant relationship between depressive symptoms and negative problem orientation, impulsiveness/carelessness style, and avoidance style. -
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. -
Deaf, Diverse, and Distinct Enhancing Diversity at Gallaudet University Through Strategic Change: Implications for Higher Education
Deaf, Diverse, and Distinct Enhancing Diversity at Gallaudet University Through Strategic Change: Implications for Higher Education I. King Jordan, Ph.D. President's and Provost's Diversity Lecture and Cultural Arts Series Ohio State University Columbus, Ohio January 26, 2006 Good afternoon. First, I want to thank President Holbrook and Provost Snyder for inviting me to speak as part of this year's Diversity Lecture and Cultural Arts Series. I am deeply honored to be included among the distinguished men and women who have given addresses during this wonderful series. It is a great pleasure to be here. I am also pleased to be here because I know that Ohio State University has long recognized disability as an aspect of human diversity. While preparing my presentation, I visited OSU's diversity website and saw that the link to disability identifies the intent "to remove physical and attitudinal barriers." I also found on the ADA coordinator's site that "…the office was created to guide the university's efforts to move beyond compliance and toward seamless access." These proactive commitments fit perfectly with what I will talk about this afternoon. I have chosen five themes as the focus of my remarks: 1. Disability from a diversity perspective 2. Diversity within the deaf community 3. The intersection of disability and other areas of diversity 4. Strategic change as the key to enhancing diversity-and the unique challenges involved in achieving this change 5. Gallaudet as a "case study" - how we are addressing these four areas, progress made, work remaining to be done, and thoughts about implications for higher education and for our nation Most of you know that throughout our history, people with disabilities have been viewed primarily from the perspective of their limitations-what they…or we… cannot do. -
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). -
Intercultural Intervention in Bi-Cultural Family Contexts with Deaf and Hearing Members: A
Intercultural Intervention in Bi-Cultural Family Contexts with Deaf and Hearing Members: A Thematic Analysis of Deaf Cultural Identity by Anita-Hosanna Kangabe Memoir submitted to the School of Social Work as part of the Master of Social Work Program Under the supervision of Marjorie Silverman University of Ottawa August 2019 Acknowledgements I wouldn’t have been able to complete this long journey without the endless support, encouragement and guidance of those closest to me. There are a few people I’d like to thank, for I couldn’t have done this without you. Dr. Marjorie Silverman, I appreciate all of the constant guidance, the time and the remarkable supervision you’ve given me. You’ve taught me so much, have helped me to believe in myself and to challenge myself. To my beloved parents, thank you for your unconditional love. Knowing that I’ve made you proud is everything to me. My fiancé, Romeo, you have been by my side every step of the way. I am forever grateful to you for your loving support. And to my sister Jocy, brothers Benjamin, Gilbert, Adelin and Orchide; I dedicate this work to you. You all hold a special place in my heart. Thank you to my maker, my God. i Abstract This exploratory research aims to analyse the applicability of intercultural approaches in family contexts with both hearing and deaf members. The study examines different perspectives of deafness, challenging the over-medicalization of deafness and seeking to review accessibility in practice. We discover alternatives through the innovative use of intercultural intervention outside of its usual ethno-cultural context where, instead, Deaf culture is concerned. -
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.