Recognition of Sign Language using Deep Neural Network

The Shape of Air: American Sign Language as Narrative Prosthesis in 21st Century North American Media

Sign language plays a crucial role as a communication tool for both the deaf and hard-of- hearing communities, enabling them to engage and interact effectively within their own community as well as with others.However, communication barriers arise when individuals unfamiliar with sign language engage with those who rely on it, underscoring the need for inclusive solutions. Real-time sign language interpretation systems, leveraging machine learning and computer vision technologies, present a promising approach to bridging this gap. These systems convert sign language gestures into spoken or written language by utilizing gesture recognition algorithms, neural networks, and natural language processing. By analyzing hand movements, facial expressions, and body language, the systems provide accurate, context-aware translations of various sign languages, such as American Sign Language (ASL), with minimal delay. This enables seamless, natural interactions, making such technologies essential for fostering inclusive communication in diverse settings. Key Words: Sign language recognition, real- time interpretation, machine learning, com- puter vision, gesture recognition, neural networks, natural language processing, communication barriers, inclusivity, American Sign Language (ASL).

Sign Language Recognition (SLR) has become an essential area of research due to its potential to promote understanding between the deaf and hearing communities through communication. This paper provides an in-depth study of various methodologies and models employed in SLR, focusing on Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN). We analyze their application to datasets from various sign languages, such as Arabic Sign Language (ArSL), American Sign Language (ASL), and British Sign Language (BSL), and explore how these models improve the recognition of dynamic, multi-dimensional hand gestures. This research not only advances the understanding of deep learning applications in sign language recognition but also addresses critical challenges in data processing and real-time applications, paving the way for inclusive technologies in informatics and human-computer interaction. Despite the progress in applying deep learning techniques to SLR, several challenges remain, particularly in dataset limitations, handling large vocabularies, and ensuring consistent performance across diverse environments and signers. The paper also investigates the broader applications of SLR, such as virtual reality, healthcare, education, and accessibility, and discusses the integration of SLR with human-computer interaction systems. Furthermore, it highlights current limitations in the field, such as difficulties with video data handling, the need for standard datasets, and issues related to training computational models. Finally, the paper outlines future research directions, including developing more robust SLR systems that can function effectively in uncontrolled environments, improving data collection methodologies, and creating real-time, user-friendly applications to assist the community of deaf and hard-of-hearing individuals.

Contents: Preface. Part I: Reminiscences. L. Fant, Two Memorable Meals With Ursula and Ed. R.M. Battison, American Sign Language Linguistics 1970-1980: Memoir of a Renaissance. B. Bragg, A Fish Story for Ursula and Other Remembrances. Part II: Historical and Comparative Analyses of Sign Languages. J. Woodward, Sign Languages and Sign Language Families in Thailand and Vietnam. D. McKee, G. Kennedy, Lexical Comparisons of Signs From American, Australian, British, and New Zealand Sign Languages. H. Lane, R.C. Pillard, M. French, Origins of the American Deaf-World: Assimilating and Differentiating Societies and Their Relation to Genetic Patterning. Part III: Language in the Visual-Spatial Modality. E.L. Newport, T. Supalla, Sign Language Research at the Millennium. P. Siple, Attentional Resources and Working Memory: A New Framework for the Study of the Impact of Deafness on Cognition. M. Wilson, K. Emmorey, When Does Modality Matter? Evidence From ASL on the Nature of Working Memory. P.B. Braem, T. Bram, A Pilot Study of the Expressive Gestures Used by Classical Orchestra Conductors. N. Frishberg, An Interpreter Creates the Space. Part IV: Linguistic Analysis of Sign Languages. S.D. Fischer, More Than Just Handwaving: The Mutual Contributions of Sign Language and Linguistics. R.B. Wilbur, Phonological and Prosodic Layering of Nonmanuals in American Sign Language. R.C. Loew, C.T. Akamatsu, M. Lanaville, A Two-Handed Manual Alphabet in the United States. E. Pizzuto, V. Volterra, Iconicity and Transparency in Sign Languages: A Cross-Linguistic Cross-Cultural View. M. Kuntze, Codeswitching in ASL and Written English Language Contact. S.K. Liddell, Indicating Verbs and Pronouns: Pointing Away From Agreement. Part V: Language Acquisition. J.G. Bettger, Viewing Deaf Children in a New Way: Implications of Bellugi and Klima's Research for Education. R.P. Meier, Shared Motoric Factors in the Acquisition of Sign and Speech. A. Abrahamsen, Explorations of Enhanced Gestural Input to Children in the Bimodal Period. D. Lillo-Martin, Early and Late in Language Acquisition: Aspects of the Syntax and Acquisition of Wh-Questions in American Sign Language. J. Reilly, Bringing Affective Expression Into the Service of Language: Acquiring Perspective Marking in Narratives. C.A. Padden, V.L. Hanson, Search for the Missing Link: The Development of Skilled Reading in Deaf Children. L.A. Petitto, On the Biological Foundations of Human Language. Part VI: The Neural Organization of Sign Language. A.R. Damasio, H. Damasio, Language and the Brain. D.P. Corina, Some Observations Regarding Paraphasia in American Sign Language. H. Poizner, D. Brentari, M.E. Tyrone, J. Kegl, The Structure of Language as Motor Behavior: Clues From Signers With Parkinson's Disease. V.A. Fromkin, On the Uniqueness of Language.

People around the world with speech and hearing impairment use a media of communication known as ‘Sign language’. In recent times, Sign language is omnipresent. However, there exists a challenge for people who do not know sign language, to communicate with people who can communicate exclusively using sign language. This gap can be bridged by using technologies of recent times to recognize gestures and design intuitive systems with deep learning. The aim of this paper is to recognise American Sign Language gestures dynamically and create an intuitive system which provides sign language translation to text and speech of various languages. The system uses Convolutional neural network, natural language processing, language translation and text-to-speech algorithms. It is capable of recognizing hand gestures dynamically and predicting the corresponding letters to form a desired sentence accurately.

People suffering from speech impairment can't communicate using hearing and speech methods, they believe signing for communication. Sign language is employed among everybody who is speech impaired, but they find a tough time in communicating with people which are non-signers (people aren’t proficient in sign language). So, requirement of a symbol language interpreter may be a must for speech impaired people. There has been favourable progress within the field of gesture recognition and motion recognition with current advancements in deep learning. There has been quite a significant development in computer vision which would enable us to easily track the hand gestures. The proposed system tries to try to a true time translation of hand gestures into equivalent English text. This system takes hand gestures as input through video and translates it text which might be understood by a non-signer. There will be use of CNN for classification of hand gestures. By deploying this technique, the communication gap between signers and non-signers will be reduced and they will be easily able to communicate with normal people.

Sign Language Recognition (SLR) systems have gained significant popularity in recent years. Despite the existence of various Deep Learning (DL) models to classify sign languages, the deployment of these models on resource-constrained devices remains a challenge. Majority of the research on SLR focuses on popular sign languages, such as American Sign Language (ASL), German Sign Language (GSL) etc., while Kannada Sign Language (KSL) remains significantly underexplored. This study focuses on creating a word-level custom dataset of 33 classes consisting of 2319 videos and training three DL models, namely, LSTM, BiLSTM, and Encoder-only Transformer for dynamic KSL recognition. Trained models were then optimized with quantization techniques and converted to .tflite format, for efficient deployment on resource-constrained devices like smartphones. Dynamic Post Training Quantization (PTQ) model, for the BiLSTM model achieved the highest accuracy of 95.71%, followed by the LSTM model with 94.7%, and the Transformer-based architecture achieved 94.19%. Transformer model deployed on smartphone achieved the smallest inference time of 16.2 ms with a model size of 1097.77 KB, followed by LSTM recording 19.6 ms as inference time with a model size of 1254.49 KB and BiLSTM recording the highest inference time of 27.8 ms with a model size of 3862.01 KB.

Losing the ability to speak exerts psychological and social impacts on the affected people due to the lack of proper communication. Thus, Sign Language (SL) is considered a boon to people with hearing and speech impairment. SL has developed as a handy mean of communication that form the core of local deaf cultures. It is a visual–spatial language based on positional and visual components, such as the shape of fingers and hands, their location and orientation as well as arm and body movements. The problem is that SL is not understood by everyone, forming a communication gap between the mute and the able people. Multiple and systematic scholarly interventions that vary according to context have been implemented to overcome disability-related difficulties. Sign language recognition (SLR) systems based on sensory gloves are significant innovations that aim to procure data on the shape or movement of the human hand to bridge this communication gap, as the proposed system. The proposed model is a glove equipped with five flex sensors, interfacing with a control unit fixed on the arm, translating American Sign Language (ASL) and Arabic Sign Language (ArSL) to both text and speech, displayed on a simple Graphical User Interface (GUI). The proposed system aims to provide an affordable and user friendly SL translator system, working on the basis of Machine Learning (ML). However, it adapts to each person’s hand instead of using a generic data set. The system achieved 95% recognition rate with static gestures and up to 88% with dynamic gestures.

Sign Language Recognition is a vast domain with a variety of solutions available on the market which achieve the same results, translating hand gestures to text and/or audio. There is a plethora of sign languages used globally, but the mostreputed and commonly used sign language is the American Sign Language (ASL). ASL uses hand gestures and finger spelling to convey the English alphabets and words. The two methods of implementing sign language translation are: Image Processing and Gesture Recognition. The gesture recognition gloves available in the current market utilize expensive flex sensors to map the bend of the users’ fingers. This makes the overall cost of the gloves expensive. Through ‘‘ASL Sign Language Recognition Gloves we aim to implement gesture recognition with MEMS accelerometers that are comparatively cheaper and make the glove more affordable.

Speech impairment limits a person’s capacity for oral and auditory communication. Improvements in communication between the deaf and the general public can be progressed by a real-time sign language detector. Recent studies have contributed to make progress in motion and gesture identification processes using Deep Learning (DL) methods and computer vision. But the development of static and dynamic sign language recognition (SLR) models is still a challenging area of research. The difficulty is in obtaining an appropriate model that addresses the challenges of continuous signs that are independent of the signer. Different signers’ speeds, durations, and many other factors make it challenging to create a model with high accuracy and continuity. This study mainly focused on SLR using a modified DL and hybrid optimization approach. Notably, spatial and geometric-based features are extracted via the Visual Geometry Group 16 (VGG16), and motion features are extracted using the optical flow approach. A new DL model, CNNSa-LSTM, is a combination of a Convolutional Neural Network (CNN), Self-Attention (SA), and Long-Short-Term Memory (LSTM) to identify sign language. This model is developed for feature extraction by combining CNNs for spatial analysis with SA mechanisms for focusing on relevant features, while LSTM effectively models temporal dependencies. The proposed CNNSa-LSTM model enhances performance in tasks involving complex, sequential data, such as sign language processing. Besides, a Hybrid Optimizer (HO) is proposed using the Hippopotamus Optimization Algorithm (HOA) and the Pathfinder Algorithm (PFA). The proposed model has been implemented in Python, and it has been evaluated over the existing models in terms of accuracy (98.7%), sensitivity (98.2%), precision (98.5%), Word Error Rate (WER) (0.131), Sign Error Rate (SER) (0.114), and Normalized Discounted Cumulative Gain (NDCG) (98%) as well. The proposed model has recorded the highest accuracy of 98.7%.

A lightweight network-based sign language robot with facial mirroring and speech system

Introduction In this paper, I examine the practices of representing Deaf ‘voices’’ to hearing audiences in two recent US television crime dramas. More literally I look at how American Sign Language is framed and made visible on the screen through various production decisions. Drawing examples from an episode of CSI: New York that aired in December 2006 and an episode of Law and Order: Criminal Intent that aired in April 2007, I examine how the practices of filming Deaf people and the use of American Sign Language intersect with the production of a Deaf ‘voice’ on the screen. The problem of representing a Deaf ‘voice’ on the screen is akin to the problem of representing other minority languages. Film and television producers in the United States have to make choices about whether the majority audience of English speakers will have access to the minority language or not. In the face of this dilemma media producers have taken several approaches: subtitling foreign speech, translating foreign speech through other characters, or leaving the language inaccessible except to those who use it. The additional difficulty with representing national sign languages is that both the language and the recording medium are visual. Sometimes, filmmakers make the choice of leaving some portions of the signed dialogue inaccessible to a non-signing hearing audience. On the one hand this choice could indicate a devaluing of the signed communication, as its specific content is considered irrelevant to the plot. On the other hand it could indicate that Deaf people have a right to be visible on television using their own language without accommodating hearing people. A number of choices made in the filming and editing can subtly undermine positive representations of Deaf ‘voices’ particularly to a Deaf audience. These choices often construct an image of sign languages as objectified, exoticised, disjointed, incomplete, or a code for spoken language. Simple choices such as using simultaneous speaking and signing by Deaf characters, cropping the scene, translating or not translating the dialogue have powerful implications for the ways that Deaf ‘voices’ are becoming more visible in the 21st century. Typical filming and editing conventions effectively silence the Deaf ‘voice.’ Over 20 years ago, in the comprehensive book, Hollywood Speaks: Deafness and the Film Entertainment Industry (1988), Schuchman’s complaint that the filming and editing techniques of the day often did not attend to preserving the visibility and comprehensibility of sign language eon the screen, still applies today. As editing techniques have evolved over the years, fr om reliance on wide and medium shots to frequent intercutting of close-ups, the tendency to cut sign language off the screen, and out of the comprehensible view of the audience, may have even increased. Recent Portrayals of Deaf People on Television During one television season in the United States between August 2006 and April 2007, 30 episodes of six different serial television programs portrayed signing Deaf characters. Three of these programs had on-going Deaf characters that appeared in a number of episodes throughout the season, while three other programs portrayed Deaf people in a one-off episode with a Deaf theme. Initial air date for the season Program and Season # of Episodes 1 14 Aug. 2006 Weeds, Season 2 5 2 20 Sep. 2006 Jericho, Season 1 13 3 28 Jan. 2007 The L Word, Season 4 9 Table 1. Dramas with Ongoing Deaf Characters during the 2006-2007 USA Television Season Initial air date Program, Season, Episode Episode Title 1 13 Dec. 2006 CSI: New York, Season 3, Episode 12 “Silent Night” 2 3 Apr. 2007 Law and Order: Criminal Intent, Season 6, Episode 18 "Silencer" 3 12 Apr. 2007 Scrubs, Season 6, Episode 16 “My Words of Wisdom” Table 2. One-off Episodes with Signing Deaf Characters during the 2006-2007 USA Television Seasons Ironically, although the shows with ongoing characters sometimes allow the Deafness of the character to be incidental to the character, it is only the one-off crime dramas that show Deaf people relating with one another as members of a vibrant community and culture based in sign language. Often, in the ongoing series, the characters remain isolated from the Deaf community and their interactions with other Deaf people are sparse or non-existent. For example, out of the 27 episodes with an ongoing Deaf character only two episodes of The L-Word have more than one Deaf character portrayed. In both Weeds and The L-Word the Deaf character is the love interest of one of the hearing characters, while in Jericho, the Deaf character is the sister of one of the main hearing characters. In these episodes though some of realities about Deaf people’s lives are touched on as they relate to the hearing characters, the reality of signing Deaf people’s social lives in the Deaf community is left absent and they are depicted primarily interacting with hearing people. The two episodes, from CSI: New York, and Law and Order: Criminal Intent, focus on the controversial theme of cochlear implants in the Deaf community. Though it is true that generally the signing Deaf community in the U.S.A. sees cochlear implants as a threat to their community, there is no record of this controversy ever motivating violent criminal acts or murder as portrayed in these episodes. In the episode of CSI: New York entitled “Silent Night” a conflict between a young Deaf man and Deaf woman who were formerly romantically involved is portrayed. The murdered young woman who comes from a Deaf family does not want her Deaf baby to have a cochlear implant while the killer ex-boyfriend who has a cochlear implant believes that it is the best option for his child. The woman’s Deaf parents are involved in the investigation. The episode of Law and Order: Criminal Intent, entitled “Silencer,” is also ultimately about a conflict between a Deaf man and a Deaf woman over cochlear implants. In the end, it is revealed that the Deaf woman is exploring the possibility of a cochlear implant. Her boyfriend projecting the past hurt of his hearing sister leaving him behind to go off and live her own life, doesn’t want his girlfriend to leave him once she gains more hearing. So he shoots the cochlear implant surgeon in the hand to prevent him from being able to perform the surgery. Then he accidentally kills him by crushing his voice box to prevent him from screaming. Analyzing Two Crime Dramas In both television dramas, the filmmakers use both sound and video editing techniques to mark the experiential difference between hearing and Deaf characters. In comparing the two dramas two techniques are evident : muting/distorting sounds and extreme close-ups on lips talking or hands signing. Though these techniques may heighten awareness of deaf experience to a non-signing audience they also point to a disabling stereotyping of the experience of being Deaf as lacking — framing their experience as hearing loss rather than Deaf gain (Bauman & Murray; Shakespeare 199). By objectifying sign language through extreme close ups American Sign Language is portrayed as something strange and unusual that separates Deaf signers from hearing speakers. The auditory silences can either jolt the hearing non-signer into awareness of the sensory aspect of sound that is missing or it can jolt them into awareness of the visual world that they often don’t really see. In the opening few scenes of the episodes both CSI: New York and Law and Order: Criminal Intent use sound editing alternately muting or distorting sounds as they cut between a ‘deaf’ auditory perspective and a ‘hearing’ perspective on the action as it unfolds. Even though the sound editing does play a part in the portrayal of Deaf people’s experience as lacking sound, the more important aspects of film production to attend to are the visual aspects where Deaf people are seen authentically signing in their own language. Scene Analysis Methodology In taking a closer look at a scene from each episode we can see exactly how the filming and editing techniques work to create an image of sign language. I have chosen comparable scenes where a Deaf individual is interviewed or interrogated by the police using a sign language interpreter. In each scene it can be assumed that all the communication is happening in both English and ASL through an interpreter, so at all times some signing should be occurring. In transcribing the scenes, I noted each point when the editor spliced different camera shots adjacent to each other. Because of the different visual aesthetics in each program where one relied heavily on continuous panning shots, I also noted where the camera shifted focus from one character to another marking the duration of screen time for each character. This allowed for a better comparison between the two programs. In my transcripts, I included both glosses of the ASL signs visible on the screen as well as the flow of the spoken English on the audio track. This enabled me to count how many separate shifts in character screen time segments contained signing and how much of these contained completely visible signing in medium shots. CSI:NY Witness Interview Scene In the first signing scene, Gina (played by Marlee Matlin) is brought in for an interview with Detective Taylor and a uniformed officer interpreter. The scene opens with a medium shot on Detective Taylor as he asks her, “What do you think woke you up?” The shot cuts to an extreme close up of her face and hands and pans to only the hands

You have accessThe ASHA LeaderFeature1 Apr 2003Serving Clients Who Use Sign Language Susanne Scott, and James H. Lee Susanne Scott Google Scholar More articles by this author and James H. Lee Google Scholar More articles by this author https://doi.org/10.1044/leader.FTR1.08062003.6 SectionsAbout ToolsAdd to favorites ShareFacebookTwitterLinked In Many speech-language and hearing professionals now encounter clients who use sign languages as their primary mode of communication. Can these professionals serve these clients, or do they need to get interpreters or make referrals? ASHA is committed to meeting the needs of culturally and linguistically diverse populations. All too often “linguistically diverse” is assumed to refer to users of spoken languages other than English. However, individuals who use a signed language, whether American Sign Language (ASL) or some other sign system, are also linguistically diverse. It is difficult, if not impossible, to determine how many people in the United States use some form of sign language to communicate. It might be easier to try to determine the number of signing individuals that are seen in hearing and speech centers and in educational placements throughout the United States. In keeping with ASHA’s vision, it is important to ensure that there are an adequate number of speech-language and hearing professionals to serve this population. Although it may be necessary to use interpreters in some situations or locales or to make referrals to larger urban areas, programs, clinics, and schools throughout the country should be striving to better and more directly serve clients who use sign. The Population Clients who use a sign language/system are a very diverse population. Not only do these individuals differ in age, gender, race, ethnicity, national origin, religion, sexual orientation, and socio-economic status, but also in the sign language/system that they use. Often the sign language/system used is determined by the individual’s identity or, in the case of a child, the educational placement. An individual who identifies as a member of the deaf community/culture in the United States will most likely use American Sign Language. Individuals who depend on their residual hearing and identify as hard of hearing or hearing impaired, will most likely use an invented sign system (i.e., SEE II, Signed English) or more English-based sign. Children who are deaf or hard of hearing may use ASL, a sign system, or English-based sign as determined by the school’s educational/communication philosophy (see sidebar for definitions). Cultural and Linguistic Competence Cultural and linguistic competence can enhance clinical practice by: increasing the bond/relationship between the clinician and the client and the client’s significant others demonstrating respect and sensitivity enhancing service provision facilitating achievement of goals and carryover/generalization of skills And, importantly, it’s the right thing to do! The National Center for Cultural Competence ( www.georgetown.edu/ research/gucdc/ nccc/faqs.html) has identified six overarching reasons why cultural competence is important to health care providers. In brief, they are: 1. To respond to current and projected demographic changes in the United States 2. To eliminate long-standing disparities in the health status of people of diverse racial, ethnic, and cultural backgrounds 3. To improve the quality of services and health outcomes 4. To meet legislative, regulatory, and accreditation mandates 5. To gain a competitive edge in the market place 6. To decrease the likelihood of liability/malpractice claims Strategies Clinicians who are able to sign should apply the following strategies in order to meet the communication needs of their clients: Respect the communication mode of your clients. The clinician should try to match the client’s mode of communication. Be willing to admit when you do not understand. Try not to “bluff” or use a passive behavior. Speech-language-hearing professionals should model appropriate communication strategies such as confirmation and repair strategies when the clinician is not able to understand the client’s signs. Use facial expressions and body movements with signs. Facial expressions and body movements are fundamental features of ASL. In spoken English, speakers often signal a question by using a rising vocal intonation. In ASL, users do so by raising the eyebrows and widening the eyes. ASL users may ask a question by tilting their bodies forward while signaling with their eyes and eyebrows. Try to be conceptually correct in the use of signs. Choose signs that appropriately convey the information you are trying to communicate. Remember each sign in a signed language/system is composed of a specific, unique combination of a hand shape, movement, palm orientation, and location. Changing any one of these aspects of the sign changes the meaning of the sign. Always fingerspell technical terms (e.g., audiogram, frequency) first before assigning a “made up” sign (e.g., “A” hand shape circling in front of the ear + sign for graph = audiogram; “F” hand shape moving like a “wave” from left to right = frequency). Always use visual aids to supplement your signs. Use pictures of the ear and the “audiogram of familiar sounds” to help explain unfamiliar concepts (e.g., type of hearing loss; sounds the client may never have heard). Working With an Interpreter If necessary, it may be best to obtain the services of a certified interpreter for the deaf. Certified interpreters follow a strict Code of Ethics developed by the Registry of Interpreters of the Deaf (RID). Interpreters can hold different types of RID certification. For example, a CI (Certificate of Interpretation) certificate indicates the interpreter can sign from English to ASL and ASL to English. A CT (Certificate of Transliteration) certificate means the interpreter has been evaluated and is proficient in interpreting from spoken English to a more English-based sign and vice versa. Some RID interpreters hold both certificates. In addition, the National Association of the Deaf also certifies interpreters using a system with varying skill levels. Finally, some states also have certification programs and tests for interpreters. It is essential to find out what is required in your area and obtain the services of qualified interpreters. When working with an interpreter, speech-language or hearing professionals should remember that they are consumers of interpreting services. Be sure to establish what you and your signing client need from the interpreter. For example, some signing individuals have intelligible speech and the interpreter would not need to voice what the signer expresses. Other times the speech may not be intelligible enough and the interpreter may need to voice the intent of the signing client. Finally, it is important to remember that the interpreter is a conduit of information between the clinician and the client. The signing client is addressed directly. The client should not be addressed through the interpreter (i.e., “Tell her…”). Instead the professional should speak directly to the client and allow the interpreter to convey the information. To facilitate this, it is often helpful to position the interpreter next to the hearing or speech-language professional and across from the signing client. This allows for a good visual environment. The signing client can see the interpreter and the professional. During interactions as much eye contact as possible should be maintained with the client. The signing client will need to divide the visual attention between the professional and the interpreter but the professional should remain focused on the client. Definitions American Sign Language (ASL) ASL is a complete, complex language that incorporates the use of hand shapes, position, palm orientation and movement, facial expressions, body movements and postures, and gestures and other visual cues to form its words. It is completely separate from English. ASL has its own rules for grammar, morphology, and sentence order. It is the first language of many North Americans who are deaf and is the fourth most commonly used language in the United States (www.aslinfo.com). Deaf Community/Culture A multi-cultural group of individuals who identify themselves as Deaf, as an ethnic identity, and not a physical condition. The Deaf community is composed of people who use American Sign Language as their primary means of communication. The Deaf Community considers itself a linguistic minority group, a separate entity because of its unique culture, language, and social norms. It is separate from other disability consumer groups by the virtue of communication process, not physical disabilities (www.signmedia.com/info/adc.htm). Finger Spelling (Dactylology) and Invented Sign Systems Finger spelling uses hand shapes to represent the letters of the English alphabet. Invented Sign Systems (Signing Exact English [SEE] and Signed English [SE]) use manual codes (use of the hands) to represent the structure of English. English-Based Signing English-based signing entails signing a combination of both English and ASL features executed in English word order. The language proficiency of the signer will determine the use of the features from either language. It is different from the invented sign systems because it does not follow a clear-cut set of principles. In the past, this type of signing has been labeled pidgin signed English (PSE) and more recently referred to as sign English or conceptually accurate sign English (CASE). It was not intended to model English or be used in an educational context or to teach English. Its purpose is for social communication. Print Publications Resources Paul P. V. (2001). Language and Deafness (3rd ed.). San Diego, CA: Thomson Learning. Google Scholar Vold F. C., Kinsella-Meier M., & Hughes Hilley M. C. (1990). Signing with your client: A practical manual for audiologists and speech/language pathologists. Washington DC: Gallaudet University Press. Google Scholar Web www.nad.org/infocenter/infotogo/dcc/difference.html http://clerccenter.gallaudet.edu/InfoToGo/549.html www.oregon.gov/DHS/odhhs/ References Author Notes Susanne Scott, is a supervising aural rehabilitationist in the department of audiology and speech-language pathology at Gallaudet University. In addition to clinical supervision, she teaches the Clinical Applications of Sign Communication classes to students in the clinical doctorate of audiology program. Contact her by e-mail at [email protected]. James H. Lee, is a clinical supervisor in the department of audiology and speech-language pathology at Gallaudet University. In addition to clinical supervision, he teaches Clinical Applications of Sign Communication to speech-language pathology graduate students. Contact him by e-mail at [email protected]. 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Sign language recognition is one of the vital research problems in computer vision systems. Sign language is used as a natural mode of communication by speech and hearing-impaired people in their daily life activities. This paper proposes a useful method to recognize static gestures with the application of Deep Learning. The pre-trained deep learning models trained on the ImageNet data and transfer learning technique are used to recognize 24 static alphabets of the American Sign Language (ASL) dataset. The publicly available benchmark American Fingerspelling dataset is used for the training of models. The paper presents the comparison of Convolutional Neural Network (CNN) models like VGG16, VGG19 and MobileNet on the same dataset and achieved average validation accuracy of 94.9% using the MobileNet model that gave good predictions using cross-validation. Also, the model has performed better than previous studies on the same dataset. The evaluation of the model performance is presented in the confusion matrix for the best trial fold that gives the prediction results of all 24 alphabets as recognition accuracy in percentage form.

Sign Language Recognition (SLR) targets on interpreting the sign language into text or speech, so as to facilitate the communication between deaf-mute people and ordinary people. The task has broad social impact, but is still very challenging due to the complexity and large variations in hand actions. Existing dataset for Sign Language Recognition (SLR) in Bangla Sign Language (BdSL) is based on RGB images. Recent research on sign language recognition has shown better recognition accuracy using depth-based features. In this paper, we present a complete dataset for Bangla sign digits from Zero (Shunno in Bangla) to Nine (Noy in Bangla) using MediaPipe, a cross-platform depth-map estimation framework. The proposed method can utilize hand skeleton joint points containing depth information in addition to x, y coordinates from RGB images only. To validate the effectiveness of our proposed approach, we have run MediaPipe on a benchmark American Sign Language (ASL) dataset. Running different classifiers in our proposed dataset we got 98.65% using Support Vector Machine (SVM). Moreover, we compared our dataset with the existing Bangla digit dataset Ishara Bochon using deep learning based approach and achieved significantly higher accuracy.