RECOGNIZING AND UNDERSTANDING AMERICAN SIGN LANGUAGE USING DEEP LEARNING AND MEDIAPIPE

Sign language is a basic method for solving communication problems between deaf and hearing people. In order to communicate, deaf and hearing people normally use hand gestures, which include a combination of hand positioning, hand shapes, and hand movements. Thai Sign Language is the communication method for Thai hearing-impaired people. Our objective is to improve the dynamic Thai Sign Language translation method with a video captioning technique that does not require prior hand region detection and segmentation through using the Scale Invariant Feature Transform (SIFT) method and the String Grammar Unsupervised Possibilistic C-Medians (sgUPCMed) algorithm. This work is the first to propose the sgUPCMed algorithm to cope with the unsupervised generation of multiple prototypes in the possibilistic sense for string data. In our experiments, the Thai Sign Language data set (10 isolated sign language words) was collected from 25 subjects. The best average result within the constrained environment of the blind test data sets of signer-dependent cases was 89–91%, and the successful rate of signer semi-independent cases was 81–85%, on average. For the blind test data sets of signer-independent cases, the best average classification rate was 77–80%. The average result of the system without a constrained environment was around 62–80% for the signer-independent experiments. To show that the proposed algorithm can be implemented in other sign languages, the American sign language (RWTH-BOSTON-50) data set, which consists of 31 isolated American Sign Language words, is also used in the experiment. The system provides 88.56% and 91.35% results on the validation set alone, and for both the training and validation sets, respectively.

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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While a significant amount of work has been done on the commonly used, tightly -constrained weather-based, German sign language (GSL) dataset, little has been done for continuous sign language translation (SLT) in more realistic settings, including American sign language (ASL) translation. Also, while CNN - based features have been consistently shown to work well on the GSL dataset, it is not clear whether such features will work as well in more realistic settings when there are more heterogeneous signers in non-uniform backgrounds. To this end, in this work, we introduce a new, realistic phrase-level ASL dataset (ASLing), and explore the role of different types of visual features (CNN embeddings, human body keypoints, and optical flow vectors) in translating it to spoken American English. We propose a novel Transformer-based, visual feature learning method for ASL translation. We demonstrate the explainability efficacy of our proposed learning methods by visualizing activation weights under various input conditions and discover that the body keypoints are consistently the most reliable set of input features. Using our model, we successfully transfer-learn from the larger GSL dataset to ASLing, resulting in significant BLEU score improvements. In summary, this work goes a long way in bringing together the AI resources required for automated ASL translation in unconstrained environments.

Accurate prediction of reservoir pressure, particularly in deep formations, is crucial for ensuring drilling safety and optimizing hydrocarbon exploration efficiency. Traditional empirical or semi-empirical methods often suffer from limited adaptability and weak generalization capability due to their reliance on simplified assumptions and predefined parameters. To address these limitations, this study proposes a deep learning framework based on a hybrid Convolutional Neural Network–Long Short-Term Memory (CNN-LSTM) architecture for reservoir pressure prediction. The model leverages the feature extraction capability of CNNand the temporal sequence learning strength of LSTM to effectively capture nonlinear patterns in well log data. A dataset comprising logging and measured pressure data from ten wells in Block A of a Chinese basin was used for training and validation. Principal Component Analysis (PCA) was employed for feature selection and dimensionality reduction. The performance of the proposed CNN-LSTM model was benchmarked against BP, CNN-BP, and CNN-RNN models using standard evaluation metrics including Mean Squared Error (MSE), Mean Absolute Error (MAE), and the coefficient of determination (R²). Results indicate that the CNN-LSTM model achieved superior predictive accuracy, demonstrating its effectiveness in handling complex geological conditions and offering a robust approach for overpressure identification in deep reservoirs.

The field of signal processing using machine and deep learning algorithms has undergone significant growth in the last few years, with a wide scope of practical applications for electroencephalography (EEG). Transcutaneous electroacupuncture stimulation (TEAS) is a well-established variant of the traditional method of acupuncture that is also receiving increasing research attention. This paper presents the results of using deep learning algorithms on EEG data to investigate the effects on the brain of different frequencies of TEAS when applied to the hands in 66 participants, before, during and immediately after 20 min of stimulation. Wavelet packet decomposition (WPD) and a hybrid Convolutional Neural Network Long Short-Term Memory (CNN-LSTM) model were used to examine the central effects of this peripheral stimulation. The classification results were analysed using confusion matrices, with kappa as a metric. Contrary to expectation, the greatest differences in EEG from baseline occurred during TEAS at 80 pulses per second (pps) or in the ‘sham’ (160 pps, zero amplitude), while the smallest differences occurred during 2.5 or 10 pps stimulation (mean kappa 0.414). The mean and CV for kappa were considerably higher for the CNN-LSTM than for the Multilayer Perceptron Neural Network (MLP-NN) model. As far as we are aware, from the published literature, no prior artificial intelligence (AI) research appears to have been conducted into the effects on EEG of different frequencies of electroacupuncture-type stimulation (whether EA or TEAS). This ground-breaking study thus offers a significant contribution to the literature. However, as with all (unsupervised) DL methods, a particular challenge is that the results are not easy to interpret, due to the complexity of the algorithms and the lack of a clear understanding of the underlying mechanisms. There is therefore scope for further research that explores the effects of the frequency of TEAS on EEG using AI methods, with the most obvious place to start being a hybrid CNN-LSTM model. This would allow for better extraction of information to understand the central effects of peripheral stimulation.

This research introduces an enhanced fault detection approach, variational mode decomposition (VMD), for identifying open-circuit IGBT faults in the grid-side converter (GSC) of a doubly fed induction generator (DFIG) wind turbine system. VMD has many advantages over other decomposition methods, notably for non-stationary signals and noise. VMD’s robustness stems from its ability to decompose a signal into intrinsic mode functions (IMFs) with well-defined centre frequencies and bandwidths. The proposed methodology integrates VMD with a hybrid convolutional neural network–long short-term memory (CNN-LSTM) architecture to efficiently extract and learn distinctive temporal and spectral properties from three-phase current sources. Ten operational scenarios with a wind speed range of 5–16 m/s were simulated using a comprehensive MATLAB/Simulink version R2022b model, including one healthy condition and nine unique IGBT failure conditions. The obtained current signals were decomposed via VMD to extract essential frequency components, which were normalised and utilised as input sequences for deep learning models. A comparative comparison of CNN-LSTM and CNN-only classifiers revealed that the CNN-LSTM model attained the greatest classification accuracy of 88.00%, exhibiting enhanced precision and resilience in noisy and dynamic environments. These findings emphasise the efficiency of integrating advanced signal decomposition with deep sequential learning for real-time, high-precision fault identification in wind turbine power electronic converters.

A common endocrine condition, thyroid dysfunction can lead to major side effects like obesity, infertility, cardiovascular disease, and cognitive decline if left untreated. Traditional diagnostic methods use biochemical assays, which are time-consuming, expensive, and invasive despite their accuracy. This paper suggests a completely automated, non-invasive screening framework that uses human voice acoustics as a thyroid dysfunction biomarker in order to overcome these difficulties. Subjects with both normal and abnormal thyroid conditions had their voice recordings taken, and they were pre-processed using spectral analysis, noise reduction, framing, and the feature extraction of Mel-Frequency Cepstral Coefficients (MFCCs). In order to capture both long-term temporal dependencies and local spectral structures in the vocal signal, a hybrid Convolutional Neural Network–Long Short-Term Memory (CNN-LSTM) model was created. While LSTM layers modeled temporal variations associated with vocal fold vibrations influenced by thyroid abnormalities, CNN layers extracted hierarchical representations from spectral features. Experiments showed that the CNN-LSTM architecture outperformed conventional classifiers like Support Vector Machines (SVM), Random Forests (RF), and K-Nearest Neighbors (KNN) by achieving an overall classification accuracy of over 98%. The results show how voice can be a trustworthy, non-invasive biomarker and how deep learning models can be used to create thyroid dysfunction diagnostic tools that are affordable, scalable, and easy for patients to use.

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.

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

Sign language recognition system for communicating to people with disabilities

Considerable attention has recently been focused on classification and regression-based convolutional neural network (CNN) and long short-term memory (LSTM) due to their excellent performance in capturing complex spatial and temporal information characteristics for structural damage identification. However, few studies have considered structural damage identification as a classification and regression problem. In addition, bridges in practical engineering are vulnerable to various environmental and vehicle loading conditions. Hence, this study proposed a new two-stage CNN–LSTM configuration for bridge damage identification using vibration data considering the influence of temperatures. First, a classification-based CNN–LSTM is designed to perform multiclass damage detection tasks, and then a regression-based CNN–LSTM is developed for damage localization and severity prediction tasks. The performance of the proposed damage identification method was evaluated through a simulation dataset of a concrete highway bridge model and a field experiment dataset of Z24-bridge (Switzerland). In addition, a set of statistical evaluation metrics such as sparse categorical cross-entropy loss, accuracy, confusion matrix, mean squared loss, mean absolute error, mean absolute percentage error, and coefficient of determination were used to compare the damage identification performance of the proposed CNN–LSTM configuration with a regular CNN model and conventional machine learning (ML) algorithms. Prediction results indicate that the proposed CNN–LSTM model outperforms the regular CNN model and conventional ML algorithms for bridge damage identification.

A major challenge faced by our society is the difficulty individuals with disabilities encounter in expressing their emotions to others. People with disabilities often depend on sign (gesture) languages for communication. This project aims to develop a model capable of recognizing and translating sign language alphabets from hand gestures into text and audio. The primary objective is to enhance communication between individuals with hearing impairments and the broader society. We assessed our model's performance using a publicly accessible Indian Sign Language (ISL) dataset. For image classification, we employed Convolutional Neural Networks (CNNs) with the Inception V3 architecture. Hand gestures are captured through webcams, and our model identifies the corresponding alphabet for each gesture. This project seeks to overcome current challenges in Sign Language Recognition and aims to enhance its effectiveness and efficiency.

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.

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

In the era of social commerce, recommendation systems must transcend traditional paradigms by incorporating multimodal data to personalize content more effectively. This study proposes a sentiment-guided, multimodal recommendation framework that integrates text and image-based features to enhance product recommendation strategies, particularly in influencer marketing and fashion retail. The proposed architecture utilizes Gated Recurrent Units (GRUs) for analyzing user-generated textual comments to capture emotional tone and sentiment directionality. Concurrently, a hybrid Convolutional Neural Network–Long Short-Term Memory (CNN-LSTM) pipeline processes product images, extracting semantic features and generating descriptive captions that encapsulate visual attributes such as color, category, and brand. By fusing textual sentiment with visual embeddings, the system produces contextually relevant and emotionally resonant recommendations. The GRU-based sentiment classifier achieves a classification accuracy of 89%, while the CNN-LSTM image captioning module attains a BLEU-1 score of 0.68 and 85% accuracy in visual attribute prediction. The real-time filtering mechanism dynamically modulates content delivery based on sentiment polarity—suppressing recommendations during negative user sentiment to reduce cognitive load and enhance user trust. Designed with scalability in mind, the system supports real-time interaction through streaming APIs and can be deployed on cloud platforms or edge devices. It enables automated visual tagging, reduces manual annotation overhead, and improves content discovery by aligning recommendations with both emotional and aesthetic user preferences. This architecture thus transforms passive browsing into an adaptive, emotionally intelligent interaction—offering a significant leap forward in personalized marketing and user engagement in visually rich environments.