American Sign Research Articles

Argument ordering in simple sentences is affected by age of first language acquisition: Evidence from late first language signers of ASL.

Research on the language acquisition of deaf individuals who are exposed to accessible linguistic input at a variety of ages has provided evidence for a sensitive period of first language acquisition. Recent studies have shown that deaf individuals who first learn language after early childhood, late first-language learners (LL1), do not comprehend reversible Subject-Verb-Object (SVO) sentences. The present study analyzed 478 signed productions elicited with pictures depicting simple events with one or two arguments by 28 signers. The argument order patterns of native signers converged with one another and the word order patterns of American Sign Language (ASL). By contrast, the ordering patterns of the LL1 signers did not converge with one another or with the patterns of the native signers. This indicates that early childhood is a period of heightened sensitivity to basic word order and may help explain why complex structures are difficult for LL1 signers to learn.

American Sign Language Recognition System: Enhancing Communication Accessibility for Individuals with Hearing Disabilities

American Sign Language Recognition System: Enhancing Communication Accessibility for Individuals with Hearing Disabilities

“Language means freedom to me” perspectives of Deaf and hard-of-hearing students on their experiences in the English as a foreign language classroom

IntroductionTeaching English as a foreign language (EFL) to Deaf and hard-of-hearing (DHH) students is regarded as a major challenge.The aim of the study is to examine the perspectives of DHH students regarding their experiences in the EFL classroom.MethodsUtilizing a qualitative design, semi-structured interviews were conducted with 17 former DHH students who learned English at German schools for the DHH.ResultsThe findings reveal various language combinations within the EFL classroom, which entirely depend on the teacher. Several critical aspects of the EFL classes were highlighted, including the insufficient foreign sign language competences of teachers, the juxtaposition of German Sign Language (DGS) signs and spoken English, and the lack of Deaf cultural content and awareness in the teaching. Additionally, the absence of interactive engagement in the EFL classroom was noted as a significant issue. Based on the DHH students’ EFL learning experiences, both English and American Sign Language (ASL) served as foreign languages for young DHH individuals, particularly in the context of international communication and social media engagement.DiscussionThis study underscores the importance of integrating ASL into EFL classrooms to better support DHH students’ language learning needs. The findings highlight the critical role of teacher training in ASL and the necessity for standardized approaches to EFL instruction. By aligning teaching practices with students’ lived experiences and incorporating sign language, educators can foster more inclusive, effective learning environments that not only enhance academic success but also affirm students’ identities and rights.

Application of AI in Turkish Sign Language Translation: A Case Study of its Use and Purpose

In Turkey, around 3 million people have hearing impairments, and the shift to digital platforms during the pandemic has worsened accessibility challenges as websites and apps often ignore their needs. Research shows that 50% of hearing-impaired individuals struggle to understand written text due to Turkish Sign Language being their first language, with Turkish serving as a second language. Differences in grammar between Turkish and Turkish Sign Language, along with a limited sign language vocabulary, further hinder comprehension. To solve this, we developed AI-powered sign language translation systems that allow people who are deaf or hard of hearing to access digital content in Turkish Sign Language. SignForDeaf's system translates text into sign language videos using Natural Language Processing (NLP) and generates seamless videos with smooth word transitions. Currently, this system supports Turkish Sign Language, with future plans to include other languages like Arabic, American, British, and Finnish Sign Language. The system was designed in collaboration with sign language experts to ensure accuracy and an inclusive development of a digital environment.

Advancing human-computer interaction: AI-driven translation of American Sign Language to Nepali using convolutional neural networks and text-to-speech conversion application

Advanced technology that serves people with impairments is severely lacking in Nepal, especially when it comes to helping the hearing impaired communicate. Although sign language is one of the oldest and most organic ways to communicate, there aren't many resources available in Nepal to help with the communication gap between Nepali and American Sign Language (ASL). This study investigates the application of Convolutional Neural Networks (CNN) and AI-driven methods for translating ASL into Nepali text and speech to bridge the technical divide. Two pre-trained transfer learning models, ResNet50 and VGG16, were refined to classify ASL signs using extensive ASL image datasets. The system utilizes the Python gTTS package to translate signs into Nepali text and speech, integrating with an OpenCV video input TKinter-based Graphical User Interface (GUI). With both CNN architectures, the model's accuracy of over 99 % allowed for the smooth conversion of ASL to speech output. By providing a workable solution to improve inclusion and communication, the deployment of an AI-driven translation system represents a significant step in lowering the technological obstacles that disabled people in Nepal must overcome.

It all made sense: ASL-first approach in classroom practice.

This qualitative study delves into the innovative Bilingual Grammar Curriculum. The curriculum aims to enhance bilingualism and linguistic skills among deaf students by integrating American Sign Language instruction and written language grammatical structures. The researcher conducted in-depth interviews with 12 experienced educators of the deaf who have implemented the curriculum in their teaching practices. Employing a thematic analysis approach, the researcher sought to uncover the educators' perceptions, experiences, and insights regarding the curriculum's effectiveness and its impact on bilingual language acquisition for deaf learners. Findings revealed a diverse range or perspectives, highlighting both the advantages and challenges of the curriculum's implementation. Themes emerged encompassing the curriculum's alignment with linguistic theories, its adaptability to diverse learning needs, the role of educator training, and the incorporation of cultural nuances within bilingual instruction. The study contributes valuable insights to the field of deaf education, shedding light on the intricate dynamics of bilingual grammar pedagogy and providing recommendations for curriculum refinement and educator professional development. Ultimately, this research underscores the significance of innovative approaches in enhancing the linguistic abilities and educational experiences of deaf and hard-of-hearing students within a bilingual framework.

A Static Sign Language Recognition Method Enhanced with Self-Attention Mechanisms.

For the current wearable devices in the application of cross-diversified user groups, it is common to face the technical difficulties of static sign language recognition accuracy attenuation, weak anti-noise ability, and insufficient system robustness due to the differences in the use of users. This paper proposes a novel static sign language recognition method enhanced by a self-attention mechanism. The key features of sign language gesture classification are highlighted by the weight function, and then the self-attention mechanism is combined to pay more attention to the key features, and the convolutional neural network is used to extract the features and classify them, which realizes the accurate recognition of different types of static sign language under standard gestures and non-standard gestures. Experimental results reveal that the proposed method achieves an average accuracy of 99.52% in the standard static sign language recognition task when tested against the standard 36 static gestures selected within the reference American Sign Language dataset. By imposing random angular bias conditions of ±(0°-9°] and ±(9°-18°], the average recognition rates in this range were 98.63% and 86.33%. These findings indicate that, compared to existing methods, the proposed method not only maintains a high recognition rate for standard static gestures but also exhibits superior noise resistance and robustness, rendering it suitable for static sign language recognition among diverse user populations.

A comparative study of how teachers communicate in deaf education classrooms.

This study investigates the communication practices of four teachers in 3rd to 6th grade classrooms with 9 deaf students with limited language proficiency and in stages of emergent writing development. Analyzing language modalities, utterance types, and class interactivity, we found that teachers using American sign language used student-centered approaches, generating a greater number of directives and responsive utterances. They persevered in increasing students' engagement and were successful in clarifying misunderstandings. Teachers using spoken English used teacher-centered approaches, making general comments directed at the whole class, which consequently reduced student participation and responsiveness. They also largely avoided repairing communication breakdowns with emergent writers, focusing instead on those with greater auditory and speaking abilities. These patterns reveal disparities in classroom communication that can affect student learning. Our findings highlight the need for teacher preparation programs to equip teachers with skill sets to employ accessible and effective communication during instruction, especially with deaf students who are still developing foundational language and writing skills.

Exploring identity building, language transmission and educational strategies for immigrant d/Deaf multilingual learners

ABSTRACT This article, grounded in sociolinguistics, examines the identity building, language transmission and educational strategies of immigrant d/Deaf multilingual learners (IDML). The scientific literature mainly focuses on a single pair of languages – the national spoken language and the national signed one. For example, Deaf Studies traditionally analyse the situation of the White Deaf user of English and American Sign Language (ASL). A major change has recently occurred with the emergence of multilingual, multicultural and inclusive Deaf Studies. At the same time, professionals and members of the school community regularly have to face the increasing diversity of d/Deaf students’ profiles. Qualitative data were collected on an IDML cohort residing in and attending primary or secondary schools in France from families who use a spoken or signed language other than French or French Sign Language (LSF) at home. The main results reveal how the building of d/Deaf identity depends on linguistic and cultural transmission within families and to what extent the diversity of IDML is considered in educational strategies at school. The goal of the present paper is to stress the urgent need for this new and crucial research area on IDML to be developed in the French context and beyond.

Sign language delays in deaf 3- to 5-year-olds with hearing parents.

Deaf children of hearing parents (DOH) are at risk for early language delays (ELD) due to environmental and etiological factors, compounding the previously reported higher incidence of ELD in deaf children of deaf parents (DOD) compared to the general population. Archival data from the online database of the Visual Communication and Sign Language Checklist yielded 147 ratings of DOH 3- to 5-year-old children whose parents reported American sign language (ASL) being used in the home at least equally to spoken language. Research goals included (1) examining the incidence of ELD in this cohort, (2) investigating differences in outcomes based on the scaling method used, (3) exploring patterns among the ratings of DOH children identified with ELD, (4) comparing the DOH outcomes to published DOD data, and (5) investigating differences between ratings and individual children. The DOH outcomes suggested an incidence of ELD twice that of the previous DOD sample, with a higher percentage of more severe delays. Even DOH children not identified with ELD demonstrated less well-developed ASL skills than their DOD peers. This emphasizes the need for ongoing monitoring of early ASL skills to allow for early identification of needs and adjustment of interventions to address ASL development.

Differences in biological motion perception associated with hearing status and age of signed language exposure.

This study investigates how American Sign Language (ASL) fluency and hearing status influence the perception of biological motion, using three point-light display (PLD) tasks. Prior research indicates that early exposure to ASL among deaf signers results in more rapid and effortless recognition of biological motion than hearing nonsigners, potentially due to the expertise in deciphering complex human movements or possibly due to neuroplasticity in deaf brains. However, it remains uncertain whether this advantage stems from signed language proficiency or the experience of being deaf. To explore this, we designed three PLD tasks involving viewing randomly moving dots, identifying a person from biological motion PLDs, and determining whether right-side up and inverted PLDs depict actions involving a ball. A diverse cohort of participants (N = 224) with varying ASL fluencies and hearing statuses completed the tasks online, providing us with reaction time and accuracy data. Our results demonstrate that earlier ASL exposure is associated with accuracy, especially on complex action identification tasks. Furthermore, we discovered robust evidence for a speed-accuracy trade-off in deaf participants, in which they performed more quickly but less accurately. The speed-accuracy trade-off was evident in the most difficult task, the action identification task. Further analysis of this deaf group revealed that earlier signed language acquisition led to higher accuracy in action identification task. We conclude that age of ASL exposure and hearing status both significantly contribute to variations in biological motion perception, with implications for understanding visual expertise and cognitive processing in both deaf and signing populations. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Transfer learning with YOLOV8 for real-time recognition system of American Sign Language Alphabet

Sign language serves as a sophisticated means of communication vital to individuals who are deaf or hard of hearing, relying on hand movements, facial expressions, and body language to convey nuanced meaning. American Sign Language (ASL) exemplifies this linguistic complexity with its distinct grammar and syntax. The advancement of real-time ASL gesture recognition has explored diverse methodologies, including motion sensors and computer vision techniques. This study specifically addresses the recognition of ASL alphabet gestures using computer vision through Mediapipe for hand movement tracking and YOLOv8 for training the deep learning model. The model achieved notable performance metrics: precision of 98%, recall rate of 98%, F1 score of 99%, mean Average Precision (mAP) of 98%, and mAP50-95 of 93%, underscoring its exceptional accuracy and sturdy capabilities.

A multi-lingual sign language recognition system using machine learning

AbstractRecently, automatic sign language recognition field gets a great attention. Machine learning algorithms are mainly used to recognize hand gestures. Most of recent studies train their machine learning model using a specific sign language of a specific country such as the American Sign Language. In this paper, we propose a multi-lingual sign language system based machine learning that is called Multi-lingual Sign Languages Interpreter (MSLI) system. MSLI trains a machine learning model based on hand signs of multiple languages. It can detect the language of the input signs and their labels. In a case of input testing signs with the same language, the proposed system can provide two-steps recognition, where it only detects the language of the first sign, and then the rest signs are tested according to the recognized language. Also, MSLI can provide separate classification of signs per each language. Experiments were performed using 11 datasets with different languages. Separate and combined classification was performed on the input data. Experimental results show the accuracy of the proposed system. Training accuracy of the proposed system over most of the used separate different sign language datasets is approximately ranged from 90 to 100%. Also, most classification accuracy results of the test data of the separate datasets exceeded 90%. The combined classification of proposed MSLI archived training accuracy of 95.87% and testing accuracy of 92.33%.

The count-mass distinction in Hong Kong Sign Language: an intra- and cross-modal comparison

Abstract This article is in part an intra- and cross-modal comparison of the count-mass distinction (CMD) in Hong Kong Sign Language (HKSL). For the intra-modal analysis, HKSL data are compared to data found in Koulidobrova’s (2021) work on the CMD in American Sign Language/ASL (Koulidobrova, Elena. 2021. Counting (on) bare nouns: Revelations from American Sign Language. In Tibor Kiss, Francis Jeffry Pelletier & Halima Husić (eds.), Things and Stuff: The Semantics of the Count-Mass Distinction, 213–231. Cambridge: Cambridge University Press). According to Koulidobrova, the existing diagnostics used in spoken language research are insufficient to bring to light CMDs in sign languages. ASL’s CMD is visible in the failure to conjoin count and mass nouns and in the ungrammaticality of partitive constructions containing mass nouns. HKSL and ASL differ from each other in a few respects, among which the two aforementioned ASL diagnostic criteria. Regarding the cross-modal comparison, HKSL and ASL belong to different categories in Chierchia’s (2010) count-mass typology (Chierchia, Gennaro. 2010. Mass nouns, vagueness and semantic variation. Synthese 174. 99–149). Chierchia’s typology is based on CMDs in spoken languages. ASL is a number-neutral language (Type III), whereas HKSL is a number marking language (Type I) and thus patterns with languages like English and Dutch. The CMD in HKSL is visible in its failure to combine mass nouns directly with numerals and count adjectives without the intervention of a classifier. Based on my data analysis, I can furthermore argue that HKSL is a number marking language but that its plural number marking is realised through zero marking.

Divided Attention in American Sign Language Processing

Divided Attention in American Sign Language Processing

Antenatal depression and drug use among deaf and hard-of-hearing birthing parents: results from a U.S. National Survey.

This study aimed to investigate antenatal depression and drug use among deaf and hard-of-hearing (DHH) birthing parents who use American Sign Language (ASL), spoken English, or bilingually both ASL and English. DHH participants in the United States responded to the Survey on Pregnancy Experiences of Deaf and Hard-of-Hearing Women. Respondents self-reported their antenatal depression diagnoses and drug use (i.e., pain relievers, cannabis, or illicit drugs) during their last pregnancy. Poisson regression models were used to estimate prevalence ratios, adjusting for age, race/ethnicity, education, marital status, and parity. The average age of respondents (n = 587) was 35 years. Respondents were predominantly non-Hispanic white (80%), college educated (60%), and married (74%). Relative to DHH English-speakers, DHH ASL-users had lower prevalence of reporting antenatal depression diagnosis (aPR = 0.40, 95% CI: 0.23 to 0.72). DHH people who reported antenatal depression diagnosis had higher prevalence of reporting antenatal drug use (PR = 2.34, 95% CI: 1.65 to 3.33). There were no significant associations between preferred language and antenatal drug use. DHH ASL-users are less likely to report receiving an antenatal depression diagnosis compared to DHH English-speakers. Given well-documented patient-provider communication barriers among DHH ASL-users, it is unclear if the lower prevalence observed in this study is the result of inadequate or inaccessible screening during pregnancy. Future work should consider universal use of linguistically appropriate screening tools for DHH birthing parents in both clinical and research settings.

Exploring the Benefits and Applications of Video-Span Selection and Search for Real-Time Support in Sign Language Video Comprehension among ASL Learners

People learning American Sign Language (ASL) and practicing their comprehension skills will often encounter complex ASL videos that may contain unfamiliar signs. Existing dictionary tools require users to isolate a single unknown sign before initiating a search by selecting linguistic properties or performing the sign in front of a webcam. This process presents challenges in extracting and reproducing unfamiliar signs, disrupting the video-watching experience, and requiring learners to rely on external dictionaries. We explore a technology that allows users to select and view dictionary results for one or more unfamiliar signs while watching a video. We interviewed 14 ASL learners to understand their challenges in understanding ASL videos, strategies for dealing with unfamiliar vocabulary, and expectations for an in-situ dictionary system. We then conducted an in-depth analysis with 8 learners to examine their interactions with a Wizard-of-Oz prototype during a video comprehension task. Finally, we conducted a comparative study with 6 additional ASL learners to evaluate the speed, accuracy, and workload benefits of an embedded dictionary search feature within a video player. Our tool outperformed a baseline in the form of an existing online dictionary across all three metrics. The integration of a search tool and span selection offered advantages for video comprehension. Our findings have implications for designers, computer vision researchers, and sign language educators.

Insights from a Pre-Pandemic K-12 Virtual American Sign Language Program for a Post-Pandemic Online Era

Insights from a Pre-Pandemic K-12 Virtual American Sign Language Program for a Post-Pandemic Online Era

A Case Study of Suspected Childhood Apraxia of Sign.

We provide a case report of "Zoe," a 4-year-old deaf child from a deaf signing family, who presented with a possible case of Childhood Apraxia of Sign (CASign). The description is based on reports from the child's speech-language pathologist, her Individualized Education Program report, and a clinician-created sign imitation task. Zoe's sign articulation errors in American Sign Language differed from those reported for typically developing deaf children and were parallel to the types of errors observed for hearing children with childhood apraxia of speech. Specifically, Zoe produced inconsistent errors across signs, substituted more complex handshapes for simple handshapes, made errors on both unmarked (common) and marked (less common) forms, produced articulatory distortions (rather than substitutions), and exhibited "groping" behaviors (a sequence of attempts to move her hands into the correct position). In addition, Zoe sometimes self-corrected her errors by manipulating her own hands, for example, using her left hand to move the thumb of her right hand into the correct position. Zoe's pattern of sign errors is consistent with an underlying deficit in motor planning and/or programming and may constitute the first reported case of CASign.

An ERP investigation of perceptual vs motoric iconicity in sign production

The type of form-meaning mapping for iconic signs can vary. For perceptually-iconic signs there is a correspondence between visual features of a referent (e.g., the beak of a bird) and the form of the sign (e.g., extended thumb and index finger at the mouth for the American Sign Language (ASL) sign BIRD). For motorically-iconic signs there is a correspondence between how an object is held/manipulated and the form of the sign (e.g., the ASL sign FLUTE depicts how a flute is played). Previous studies have found that iconic signs are retrieved faster in picture-naming tasks, but type of iconicity has not been manipulated. We conducted an ERP study in which deaf signers and a control group of English speakers named pictures that targeted perceptually-iconic, motorically-iconic, or non-iconic ASL signs. For signers (unlike the control group), naming latencies varied by iconicity type: perceptually-iconic < motorically-iconic < non-iconic signs. A reduction in the N400 amplitude was only found for the perceptually-iconic signs, compared to both non-iconic and motorically-iconic signs. No modulations of N400 amplitudes were observed for the control group. We suggest that this pattern of results arises because pictures eliciting perceptually-iconic signs can more effectively prime lexical access due to greater alignment between features of the picture and the semantic and phonological features of the sign. We speculate that naming latencies are facilitated for motorically-iconic signs due to later processes (e.g., faster phonological encoding via cascading activation from semantic features). Overall, the results indicate that type of iconicity plays role in sign production when elicited by picture-naming tasks.