Relevant bibliographies by topics / American Sign Language (ASL)

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'American Sign Language (ASL)'

Author: Grafiati
Published: 3 June 2025
Last updated: 22 June 2025

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Journal articles on the topic "American Sign Language (ASL)"

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Crabtree, Margaret Ruth, and Ronnie B. Wilbur. "#ALL versus ALL in American Sign Language (ASL)." Proceedings of the Linguistic Society of America 5, no. 1 (2020): 798. http://dx.doi.org/10.3765/plsa.v5i1.4761.

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This paper extends a visible pattern (‘iconicity’) that has been observed in sign language verbs and adjectives to quantification in American Sign Language (ASL). The Event Visibility Hypothesis (EVH) states that boundedness is morphophonologically encoded in articulation of a rapid deceleration of movement at the end of a sign (aka end-marking). Here the EVH is applied to the two ASL quantifiers glossed #ALL and ALL. Doing so accounts for the semantic distinction between them: ALL is definite (bounded), whereas #ALL is underspecified for definiteness (unbounded).
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Padden, Carol A. "The ASL lexicon." Sign Language and Linguistics 1, no. 1 (1998): 39–60. http://dx.doi.org/10.1075/sll.1.1.04pad.

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This paper explores a range of Foreign vocabulary in American Sign Language and demonstrates that there are ways of accounting for them without undermining the fundamental independence of a natural sign language. Arguments are made for a unified lexicon in which Native and Foreign vocabulary are arranged schematically as extending from a core to a periphery with gradations of conformity to phonological constraints on ASL forms. At the conclusion of the paper there is a brief review of issues concerning the presence of Foreign vocabulary in natural sign languages.
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Pirot, Khunaw Sulaiman, and Wirya Izzaddin Ali. "The Phonological Structure of American Sign Language -ASL and zmânî âmâžaî kurdî - ZAK." Journal of University of Raparin 9, no. 4 (2022): 155–87. http://dx.doi.org/10.26750/vol(9).no(4).paper8.

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This paper deals primarily with the phonological structure of American Sign Language (ASL) and zmânî âmâžaî kurdî (ZAK) -Kurdish Sign Language. It is concerned with sign language (SL) and the types of sign language. One type is primary sign languages which are used by the Deaf people. Sign language is a visual-gestural language which relies on the use of the hands, facial expressions and body movements.Generally, there are myths about SLs. People believe that SLs are universal and have no grammatical structure. However, sign languages, as spoken languages, have lexicon, phonology, morphology and syntax. As far as the phonological structure of SLs includes handshape, location, movement and orientation of the hand. Therefore, some questions have been raised focusing on ASL and ZAK phonological structure. One of the questions is that: are the parameters of the phonological structure of ASL and ZAK the same? The aim of the paper is to apply ASL phonological aspects on ZAK. In this study, a mixed method is adopted. What is related to the hypothesis is that ZAK has three phonological parameters. One of the findings is that both ASL and ZAK have the same phonological parameters. As far as recommendation is concerned, more work can be carried out on the differences between ASL and ZAK at all linguistic levels.
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Stewart, Jesse. "A quantitative analysis of sign lengthening in American Sign Language." Sign Language & Linguistics 17, no. 1 (2014): 82–101. https://doi.org/10.5281/zenodo.268582.

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In spoken languages, disfluent speech, narrative effects, discourse information, and phrase position may influence the lengthening of segments beyond their typical duration. In sign languages, however, the primary use of the visual-gestural modality results in articulatory differences not expressed in spoken languages. This paper looks at sign lengthening in American Sign Language (ASL). Comparing two retellings of the Pear Story narrative from five signers, three primary lengthening mechanisms were identified: <em>elongation</em>, <em>repetition</em>, and <em>deceleration</em>. These mechanisms allow signers to incorporate lengthening into signs which may benefit from decelerated language production due to high information load or complex articulatory processes. Using a mixed effects model, significant differences in duration were found between (i) non-conventionalized forms vs. lexical signs, (ii) signs produced during role shift vs. non-role shift, (iii) signs in phrase-final/initial vs. phrase-medial position, (iv) new vs. given information, and (v) (non-disordered) disfluent signing vs. non-disfluent signing. These results provide insights into duration effects caused by information load and articulatory processes in ASL.
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Corina, David P., and Eva Gutierrez. "Embodiment and American Sign Language." Gesture 15, no. 3 (2016): 291–305. http://dx.doi.org/10.1075/gest.15.3.01cor.

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Little is known about how individual signs that occur in naturally produced signed languages are recognized. Here we examine whether sign understanding may be grounded in sensorimotor properties by evaluating a signer’s ability to make lexical decisions to American Sign Language (ASL) signs that are articulated either congruent with or incongruent with the observer’s own handedness. Our results show little evidence for handedness congruency effects for native signers’ perception of ASL, however handedness congruency effects were seen in non-native late learners of ASL and hearing ASL-English bilinguals. The data are compatible with a theory of sign recognition that makes reference to internally simulated articulatory control signals — a forward model based upon sensory-motor properties of one’s owns body. The data suggest that sign recognition may rely upon an internal body schema when processing is non-optimal as a result of having learned ASL later in life. Native signers however may have developed representations of signs which are less bound to the hand with which it is performed, suggesting a different engagement of an internal forward model for rapid lexical decisions.
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Ansari, Fatima, Anwar Hussain Mistry, Yusuf Mirkar, and Alim Merchant. "Real Time ASL (American Sign Language) Recognition." International Journal of Computer Sciences and Engineering 7, no. 2 (2019): 848–51. http://dx.doi.org/10.26438/ijcse/v7i2.848851.

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Ansari, Mehtab, and Neetu Raj Bharti. "AMERICAN SIGN LANGUAGE (ASL) RECOGNITION USING OPENCV." International Journal of Engineering Applied Sciences and Technology 8, no. 2 (2023): 169–73. http://dx.doi.org/10.33564/ijeast.2023.v08i02.024.

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One of the biggest challenges for thosewho are deaf or dumb is the lack of access to communication. Here, we'll createa OpenCV based system for converting American sign language to text. Computer vision model for the dumb and deaf are a revolutionary system that has the potential to improve the quality of life for those whoare affected by hearing or speech impairments. The computer vision model will detect hand movements and translate them into text in real-time, allowing deaf and dumb individuals to communicate with others more effectively. Traditionalforms of communication such as writing can be difficult and time-consuming, and they also rely on the presence of an interpreter or someone who can read orwrite. Computer vision supply a more efficient and immediate solution to this problem. The CV model are designed to detect hand movements and translate them into text, depending on the user's preferences. This allows deaf and dumb individuals to communicate with others in real-time, without the need for aninterpreter or someone who can read orwrite. The system is configured to display the text or speech on a nearby device, so that others can see and understand the message as well.
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Journal, IJSREM. "American Sign Language (ASL) Detection System using Machine Learning." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 07, no. 12 (2023): 1–13. http://dx.doi.org/10.55041/ijsrem27702.

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One of the main challenges of communicating with people who have hearing disabilities is to understand their sign language. This paper presents a dedicated research project to investigate the difficulties involved in recognizing characters in American Sign Language (ASL), which is the most widely used sign language in the world. Sign language is essential for communication among people with hearing or speech impairments, but it can be hard for those who are not familiar with it, as the signs made by people with disabilities may look complicated or messy. Effective communication requires a two-way exchange. This paper proposes a Sign Language detection system that uses American Sign Language, where users can take pictures of hand gestures through a web camera and get them analyzed. The system aims to predict and show the name that matches the picture. The proposed research proposes a method for detecting sign language that uses the collaborative features of the OpenCV and MediaPipe frameworks. The Convolutional Neural Network (CNN) is used to train the model and identify the pictures. The proposed methodshows a high detection rate and excellent accuracy. Keywords: American Sign Language, OpenCV, MediaPipe, Convolutional Neural Network
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Schlenker, Philippe. "Strong pronominals in ASL and LSF?" Sign Language and Linguistics 21, no. 2 (2018): 380–90. http://dx.doi.org/10.1075/sll.00025.sch.

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Abstract Theories of pronominal strength (e.g., Cardinaletti &amp; Starke 1999) lead one to expect that sign language, just like spoken language, can have morphologically distinct strong pronominals. We suggest that American Sign Language (ASL) and French Sign Language (LSF) might have such pronominals, characterized here by the fact that they may associate with only even in the absence of prosodically marked focus.
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Alan, Wilson. "Sign Language to Text Conversion using CNN." Indian Journal of Data Mining (IJDM) 4, no. 1 (2024): 9–12. https://doi.org/10.54105/ijdm.A1634.04010524.

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<strong>Abstract: </strong>Sign language is a communication strategy used by those who are unable to hear. So those people who know sign language can communicate with people who are deaf. But a majority of our people don&rsquo;t know sign language therefore there comes a communication gap between the ones who know sign language and others who don&rsquo;t know. This project's major purpose is to bridge this gap by developing a systemthat recognizesmultiple sign languages and translates them into text in real-time. We use machine learning technologies to construct this system especially, convolutional neural networks (cnns), which are used to recognize and translate American Sign Language (ASL) into text by capturing it using a webcam. The transformed text is then presented on the screen by which individuals can comprehend and communicate with those who use sign language. The system's performance is evaluated on a dataset of ASL gestures, attaining excellent accuracy and indicating its potential for practical usage in enhancing communication accessibility for the deaf and hard-of-hearing community.
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Dissertations / Theses on the topic "American Sign Language (ASL)"

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DiBlasi, Anita F. "Evaluating the Effects of Aging on American Sign Language Users." The Ohio State University, 2011. http://rave.ohiolink.edu/etdc/view?acc_num=osu1310670070.

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Adams, Hadiya Annvela. "EXAMINING SIGNER-SPECIFICITY EFFECTS IN THE PERCEPTION OF WORDS IN AMERICAN SIGN LANGUAGE." Cleveland State University / OhioLINK, 2012. http://rave.ohiolink.edu/etdc/view?acc_num=csu1336747548.

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Benitez-Quiroz, Carlos Fabian. "A Computational Study of American Sign Language Nonmanuals." The Ohio State University, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=osu1436909704.

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Nix, Michael Albert. "Parsing an American Sign Language Corpus with Combinatory Categorial Grammar." BYU ScholarsArchive, 2020. https://scholarsarchive.byu.edu/etd/8407.

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Research into parsing sign language corpora is ongoing. Corpora for German Sign Language and Italian Sign Language have been parsed (Bungeroth et al., 2006; Mazzei, 2011, 2012, respectively). However, research into parsing a corpus of American Sign Language is non-existent. Examples of parsed ASL sentences in literature are typically isolated examples used to show a particular type of construction. Apparently no attempt has been made to parse an entire corpus of American Sign Language utterances. This thesis presents a method for constructing a grammar so that a parser implementing Combinatory Categorial Grammar can parse a corpus of American Sign Language. The results are evaluated and presented.
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DiLoreto, Elizabeth. "American Sign Language as a Foreign Language Requirement: Curriculum, Pedagogy, and Standards." Miami University / OhioLINK, 2013. http://rave.ohiolink.edu/etdc/view?acc_num=miami1364150201.

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Bonner, Brooke Alexis. "AMERICAN SIGN LANGUAGE CORE STANDARDS AND EVIDENCE BASED INSTRUCTION." Miami University / OhioLINK, 2014. http://rave.ohiolink.edu/etdc/view?acc_num=miami1397302571.

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Geier, Colleen Avilla. "An Evaluation of an American Sign Language Interpreting Internship Program." ScholarWorks, 2016. https://scholarworks.waldenu.edu/dissertations/3096.

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This study was a program evaluation of an American Sign Language internship program that was established in 2006 at a 4-year private college in the Midwestern United States but had never been evaluated. The purpose of the study was to evaluate the effectiveness of this internship program in preparing students for employment in the field of interpreting. An expertise-oriented program evaluation case study was conducted using the lens of experiential learning theory. Research questions were used to investigate the strengths and weaknesses of the program and the ways in which the policies, objectives, and assignments prepare students to work as interns and later as professional interpreters. Semi-structured interviews were conducted with 2 former administrators who helped establish the program, 13 graduates of the program between 2013 and 2015, and 8 of the internship site directors who worked with interns between 2013 and 2015. The interview data were coded and analyzed following Merriam's approach to identify themes, and document review was used to support the themes. Key findings were that the program provided effective training for interns transitioning to professional employment, but students tended to lack self-confidence in their performances Interviewees also indicated that program documents were helpful but difficult to use, and mentors needed guidance in giving constructive feedback. An evaluation report was constructed as a research project deliverable to provide specific recommendations for program enhancement. The study promotes positive social change by providing stakeholders with the evidence-based data needed to implement further growth for the internship program, and to more effectively train interpreters to work with the Deaf community.
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Gaines, Sarah Elizabeth, and Sarah Elizabeth Gaines. "Rapid Automatized Naming (RAN) and Reading with Deaf Students Using American Sign Language (ASL)." Diss., The University of Arizona, 2016. http://hdl.handle.net/10150/621789.

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This study was an investigation of the relationship between rapid automatized naming (RAN) and reading in a sample of deaf and hard of hearing (DHH) students who use American Sign Language (ASL). Thirty DHH students, 10 to 18 years old, were given a series of assessments including measures of RAN, reading decoding, reading fluency, reading comprehension, expressive vocabulary, receptive vocabulary, and visual-motor integration. Significant correlations were found between RAN colors and reading decoding; RAN colors and reading comprehension; and RAN colors, numbers, and letters and reading fluency. A significant difference was found between symbolic (letters, numbers) and non-symbolic (objects, colors) RAN in this sample, with better performance noted on tasks of symbolic RAN. Hierarchical regression models were created for each type of RAN. Each model as a whole was significant. The proposed model for RAN objects accounted for 26.6% of the variance in RAN performance. The model for RAN colors accounted for 54.1% of the variance in RAN performance. The proposed model for RAN numbers accounted for 53% of the variance in RAN. The model for RAN letters accounted for 32.6% of the variance in RAN. Across all models, reading fluency and vocabulary were unique and statistically significant contributors in the model predicting RAN. Visual-motor integration performance was not a unique contributor to the model.
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Alfano, Alliete Rodriguez. "Communication and Culture: Implications for Hispanic Mothers with Deaf Children." Scholarly Repository, 2007. http://scholarlyrepository.miami.edu/oa_dissertations/61.

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The majority of deaf children are born to hearing parents. The fact that many of these children use sign language as their primary form of communication poses a unique language barrier between them and their hearing families. In addition, for children who are born into Hispanic families, these children have limited access to Hispanic and Deaf cultures unless their families actively pursue involvement with those communities. Data were collected through ethnographic interviews and limited participant observation and analyzed by means of grounded theory methodology. The study investigated how Hispanic mothers communicate with their deaf children who use ASL as their primary language, as well as how these mothers view Deafness as a culture.
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Brightman, Beth Lilessie Cagle. "The Relationship Between Attitudes and Perspectives of American Sign Language University Students Towards Deaf People." Scholar Commons, 2013. http://scholarcommons.usf.edu/etd/4868.

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Books on the topic "American Sign Language (ASL)"

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Byrom, Franklin. Basic ASL Dictionary. Xlibris, 2008.

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1949-, Bailey Carole Sue, Dolby Kathy 1949-, Campbell Hilda Marian 1927-, and Canadian Cultural Society of the Deaf., eds. The Canadian dictionary of ASL. University of Alberta Press, 2002.

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Cokely, Dennis, Laura Cleary, and Clayton Valli. ASL pah! Sign Media/Linstok Press, 1992.

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1959-, Chearney Lee Ann, Setzer Paul M, and Amaranth (Brooklyn, N.Y.), eds. Communicating in sign: Creative ways to learn American Sign Language (ASL). Fireside, 1998.

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Valli, Clayton. Linguistics of American sign language: A resource text for ASL users. Gallaudet University Press, 1992.

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ASL in Schools: policies and curriculum (1992 San Diego, Calif.). ASL in schools: Policies and curriculum : conference proceedings, October 28-30, 1992. Galluadet University, Continuing Education and Outreach, 1993.

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M, Jones David, and Cook Peter. United States of ASL poetry and other tales. P.C. Productions, 1998.

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Media, ASL, ed. Teach me English in ASL. 3rd ed. ASL Media, 2012.

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Bilingual Considerations in the Education of Deaf Students (1990 Las Vegas, Nev.). Bilingual considerations in the education of deaf students: ASL and English, June 28-July 1, 1990. College for Continuing Education, Continuing and Summer Studies, Gallaudet University, 1992.

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Sign, Media Inc. ASL across America: San Francisco. Sign Media, 1989.

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Book chapters on the topic "American Sign Language (ASL)"

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Mueller, Vannesa T. "American Sign Language (ASL)." In Encyclopedia of Autism Spectrum Disorders. Springer New York, 2017. http://dx.doi.org/10.1007/978-1-4614-6435-8_1654-3.

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Do, Lynna Lan Tien Nguyen. "American Sign Language (ASL)." In Encyclopedia of Child Behavior and Development. Springer US, 2011. http://dx.doi.org/10.1007/978-0-387-79061-9_115.

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Mueller, Vannesa T. "American Sign Language (ASL)." In Encyclopedia of Autism Spectrum Disorders. Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4419-1698-3_1654.

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Mueller, Vannesa T. "American Sign Language (ASL)." In Encyclopedia of Autism Spectrum Disorders. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-319-91280-6_1654.

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Hochgesang, Julie A., Ryan Lepic, and Emily Shaw. "Chapter 11. W(h)ither the ASL corpus?" In Advances in Sign Language Corpus Linguistics. John Benjamins Publishing Company, 2023. http://dx.doi.org/10.1075/scl.108.11hoc.

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In this chapter, we discuss logistic and ideological practices used by signed language corpus projects around the world to develop their corpora, starting with Johnston’s early call to develop signed language corpora in 2004. We then outline a brief history of existing American Sign Language (ASL) corpora or corpus-like collections, most of which are specialized and/or inaccessible. We unpack the lessons we have learned as we, North American linguists interested in signed language research and corpora, have sought to create a national-level corpus following the standards set by earlier projects. We conclude that our contexts are somewhat different, and describe projects that we think would be better suited for our ASL communities. In other words, though our dreams of having a single national-level ASL corpus have ‘withered’, we remain optimistic that corpus methods can be used to catalog and analyze the wealth of ASL signing data currently available.
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Adamo-Villani, Nicoletta, and Ronnie B. Wilbur. "ASL-Pro: American Sign Language Animation with Prosodic Elements." In Universal Access in Human-Computer Interaction. Access to Interaction. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-20681-3_29.

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Kaur, Gurleen, Harshit Saini, and Mohd Jubair Alam. "American Sign Language (ASL) Recognition Using Convolutional Neural Network (CNN)." In Advances in Intelligent Systems Research. Atlantis Press International BV, 2025. https://doi.org/10.2991/978-94-6463-700-7_15.

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Chaudhary, Nishu, Ridham Sheel, Ranjan Kumar, and Poornima Mittal. "Transcription of American Sign Language (ASL) Using Convolutional Neural Networks (CNNs)." In Lecture Notes in Electrical Engineering. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-99-7077-3_40.

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Huenerfauth, Matt. "Improving Spatial Reference in American Sign Language Animation through Data Collection from Native ASL Signers." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-02713-0_56.

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Kacorri, Hernisa, Allen Harper, and Matt Huenerfauth. "Measuring the Perception of Facial Expressions in American Sign Language Animations with Eye Tracking." In Universal Access in Human-Computer Interaction. Design for All and Accessibility Practice. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-07509-9_52.

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Conference papers on the topic "American Sign Language (ASL)"

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Kaur, Gurjot, Neha Sharma, Sonal Malhotra, Swati Devliyal, and Rupesh Gupta. "American Sign Language (ASL) Detection using DenseNet201 Deep Learning Architecture." In 2024 Asian Conference on Intelligent Technologies (ACOIT). IEEE, 2024. https://doi.org/10.1109/acoit62457.2024.10939359.

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Tanzer, Garrett. "FLEURS-ASL: Including American Sign Language in Massively Multilingual Multitask Evaluation." In Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers). Association for Computational Linguistics, 2025. https://doi.org/10.18653/v1/2025.naacl-long.314.

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Kezar, Lee, Nidhi Munikote, Zian Zeng, Zed Sehyr, Naomi Caselli, and Jesse Thomason. "The American Sign Language Knowledge Graph: Infusing ASL Models with Linguistic Knowledge." In Findings of the Association for Computational Linguistics: NAACL 2025. Association for Computational Linguistics, 2025. https://doi.org/10.18653/v1/2025.findings-naacl.389.

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Atia, Osama, Hussam Asskar, Oussame El Kharchy, and Wisam Elmasry. "ASL UNDERPRESSURE: Gamification of American Sign Language Learning Through Human-Computer Interaction." In 2025 7th International Congress on Human-Computer Interaction, Optimization and Robotic Applications (ICHORA). IEEE, 2025. https://doi.org/10.1109/ichora65333.2025.11017096.

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Yin, Kayo, Terry Regier, and Dan Klein. "American Sign Language Handshapes Reflect Pressures for Communicative Efficiency." In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). Association for Computational Linguistics, 2024. http://dx.doi.org/10.18653/v1/2024.acl-long.839.

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Johnson, Larry Emerson, and Sherif Rashad. "An Innovative System for Real-Time Translation from American Sign Language (ASL) to Spoken English using a Large Language Model (LLM)." In 2024 IEEE 15th Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON). IEEE, 2024. http://dx.doi.org/10.1109/uemcon62879.2024.10754690.

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Park, Yong Joon. "PRE-SERVICE TEACHERS OF EARLY CHILDHOOD EDUCATION'S PERSPECTIVES OF USING MUSIC WITH AMERICAN SIGN LANGUAGE (ASL) ​AND/OR MUSICAL INSTRUMENT(S) AS AN INTERVENTION." In 17th annual International Conference of Education, Research and Innovation. IATED, 2024. https://doi.org/10.21125/iceri.2024.0197.

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Rajurkar, Anushka, Atharva Hardikar, Kritarth Gupta, Artika Singh, and Yashodhan Karulkar. "Sign Language 2.0: Rethinking ASL Education for the Modern Age." In 2024 First International Conference for Women in Computing (InCoWoCo). IEEE, 2024. https://doi.org/10.1109/incowoco64194.2024.10863052.

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Karche, Avishkar S., Avadhoot V. Kamble, Krupa A. Maru, Sumedh S. Kedari, and Dikshendra D. Sarpate. "American Sign Language Recognition Application." In 2025 International Conference on Emerging Smart Computing and Informatics (ESCI). IEEE, 2025. https://doi.org/10.1109/esci63694.2025.10988218.

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Ratnasingam, Sabaskaran, Dilaxshan Sakajarasa, Didula Chamara, and Aruna Ishara Gamage. "Developing Accurate Sri Lankan Sign Language to Tamil Vocal And American Sign Language Translation." In 2024 International Conference on Engineering and Emerging Technologies (ICEET). IEEE, 2024. https://doi.org/10.1109/iceet65156.2024.10913736.

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Reports on the topic "American Sign Language (ASL)"

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Swannack, Robyn, Alys Young, and Claudine Storbeck. A scoping review of deaf sign language users’ perceptions and experiences of well-being in South Africa. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, 2022. http://dx.doi.org/10.37766/inplasy2022.11.0082.

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Background: This scoping review concerns deaf adult sign language users from any country (e.g. users of South African Sign Language (SASL), British Sign Language (BSL), American Sign Language (ASL) and so forth). It concerns well-being understood to include subjective well-being and following the WHO’s (2001) definition of well-being as “mental health as a state of well-being in which every individual realises his or her own potential, can cope with the normal stresses of life, can work productively and fruitfully and is able to make a contribution to his or her community.” Well-being has three components (Steptoe, Deaton, and Stone, 2015; Stewart-Brown, Tennant, Tennant, Platt, Parkinson and Weich, 2009): (i) Live evaluation, also referred to life satisfaction, which concerns an individual’s evaluation of their life and their satisfaction with its quality and how good they feel about it; (ii) hedonic well-being which refers to everyday feelings or moods and focuses on affective components (feeling happy); (iii) eudaimonic well-being, which emphasises action, agency and self-actualisation (e.g. sense of control, personal growth, feelings of purpose and belonging) that includes judgments about the meaning of one’s life. Well-being is not defined as the absence of mental illness but rather as a positive state of flourishing that encompasses these three components. The review is not concerned with evidence concerning mental illness or psychiatric conditions amongst deaf signers. A specific concern is deaf sign language users’ perceptions and experiences of well-being.
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