Hearing Community Research Articles

SIGN LANGUAGE TO TEXT CONVERTOR

Sign language is a vital form of communication for the Deaf and Hard of Hearing community, yet there remains a significant barrier in real time interaction with non-sign language users. This project proposes the development of a Sign Language to Text Converter, an innovative system designed to translate sign language gestures into written text. The goal of the system is to produce an accessible real-time tool that transcends the communication gap between the Deaf people and those who are not familiar with sign language, thus enhancing interaction in diverse spheres such as education, healthcare, and social settings. This system will utilize techniques such as computer vision and machine learning in recognizing and interpreting hand gestures. The system processes real-time video input using OpenCV for image processing and MediaPipe for hand and pose detection, extracting key features such as hand positions, movements, and orientations. These features are then fed into a Convolutional Neural Network (CNN) trained to classify individual sign language gestures. The output is translated to text, which can either be displayed on a screen or used with the text-to-speech systems to provide an auditory output. Key Words: MediaPipe, Convolutional Neural Network (CNN), Text To Speech(TTS), American Sign Language (ASL)

Public common-sense assumptions about mathematics

Circulating public discourses about mathematics and mathematics learning shape how families and students make sense of their experiences with schooling. In the United States, these discourses can play a large role in how public education policy is developed due to the commitment of public school boards to hearing community voices as well as a recent (but not new) increase in the organization of well-funded conservative parent groups working to maintain–and exacerbate–the inequitable educational opportunities that persist in this country. In this paper we analyze public discourse around mathematics learning in one New York City local school board meeting. Using tools from interaction analysis, we examined the discourse surrounding a proposal to reinstate test-based screening for middle school admissions. We delineated two key features of “common-sense” assumptions around mathematics learning that circulated in this meeting–math learners stay on a one-dimensional learning trajectory, and with varying rates of advancement–and investigated how these assumptions played out in the construction of a figured world of “schooled mathematics.” We argue that the consequences that necessarily follow from these common-sense assumptions construct mathematics as hierarchical and fixed, placing learners on a one-dimensional learning trajectory. Finally, we locate this set of emergent assumptions in the neoliberal racial project and consider the ways in which they shape a particular imagination of schooling and mathematics under neoliberalism.

The ASL Dataset for Real-Time Recognition and Integration with LLM Services

This study aims to investigate the impact of hand gesture recognition techniques on the efficiency of American Sign Language (ASL) interpretation, addressing a critical gap in the existing literature. The research seeks new insights into the challenges of automated sign language recognition, contributing to a deeper understanding of accessibility in communication for the deaf and hard-of-hearing community. The study employs a quantitative approach, using a dataset comprising hand gesture images representing the static letters of the ASL alphabet collected from multiple users. Data were collected from various individuals to ensure diversity and analyzed using machine learning models to evaluate their effectiveness in recognizing ASL signs. The results reveal that the machine learning models implemented achieved a high accuracy rate in recognizing hand gestures, indicating that person-specific variations do not significantly hinder performance. These findings provide evidence that the proposed dataset and methodologies can improve the reliability of sign language recognition systems, offering significant implications for the development of more inclusive communication technologies. This research offers a novel perspective on sign language recognition, providing valuable insight that extends the current understanding of gesture-based communication systems. The study’s findings contribute to advancements in accessibility technologies, highlighting areas for future research and practical applications in improving communication for the Deaf and hard of hearing community.

Unveiling Community Needs and Aspirations: Card Sorting as a Research Method for Developing Digital Learning Spaces

<p style="text-align:justify">This pilot study is part of a larger “Decolonization of Digital Learning Spaces” project, which aims to develop research tools for communities that are remote and/or excluded geographically, politically, economically, socially, culturally, and linguistically. The project’s ultimate goal is to work alongside these communities to design their own digital learning tools, networks, and online educational environments by accessing and leveraging their knowledge and skills. Testing the single-criterion card sorting method is the first step toward this goal. Card sorting is an easy, enjoyable, and cost-effective method for data collection and analysis, particularly for researchers working in remote areas with limited access to electricity or the Internet. The pilot explored single-criterion card sorting as a method to elicit knowledge from two diverse cultural and linguistic groups engaged in learning activities within their communities. These groups were from a Deaf and Hard of Hearing (DHH) community in Canada (engaged in a bow-making workshop) and a rural Kabyle community in Algeria (engaged in a traditional cooking lesson). Despite low participant numbers, distinct patterns emerged, indicating the method's effectiveness. The results, though anticipated, were non-random, demonstrating the potential of card sorting in producing patterns indicative of how individuals and/or communities categorize their world(s). Kabyle sortings focused on ingredients, highlighting older individuals as teachers passing along knowledge, while the DHH sortings emphasized face-to-face contact and hand movements in communication. The findings, though modest, established relationships, provided insights into the research context and offered logistical understanding, paving the way for further work with DHH and Kabyle communities towards the design of digital learning spaces.</p>

SignifAI: French sign language translation based on deep learning and large language models

In this project, we propose SignifAI which is an AI-powered software that translates French Sign Language into spoken French. The goal of SignifAI is to remove the language barrier that separates the deaf and hearing communities. The translation process proves to be a major challenge for existing methods, as most of them rely on traditional Natural Language Processing (NLP) methods which solve the task in a similar way as spoken languages (e.g., paying more attention to the logics and reasoning). The drastic syntactical difference between sign and spoken languages, however, makes such approaches not as effective and efficient. In SignifAI, we propose to leverage a combination of deep learning models for sign gesture recognition and large language models (LLM) for sentence generation from recognized words. The former allows for precise detection of individual words, while the latter takes the recognized words as inputs and returns a fluently reformulated translated sentence. With extremely limited data available for French sign language, we created our own enhanced dataset through synthetically augmenting existing videos. Utilizing the SlowFast model for this task, we have achieved a maximum test accuracy of 98.9%, and a fluid translation process after implementing ChatGPTs API. The potential applications of SignifAI range from aiding communication between the deaf and the hearing, to helping the deaf place phone calls in numerical services. Ultimately, SignifAI strives to improve all aspects of a deaf persons life, and compensates for their speaking disabilities.

A Sign Language Recognition using Improved Grey Wolf Optimization based neural networks

Sign language was developed to enable the deaf and hard of hearing community to communicate with society and convey information. It is the primary means by which they can interact with each other, as well as with the general population. The automatic gesture recognition system described in this paper uses Gray Wolf Optimization (GWO) to identify the most relevant features from a set of gesture images, with the aim of improving model performance. The main steps of the system are explained, including image preprocessing, feature extraction, GWO-based feature selection, classifier training, and evaluation. The proposed model achieved an accuracy of 99.9%.

A deaf-centred art-science approach to community engagement with sign language technologies

Sign Language Machine Translation (SLMT) is an emerging technology that will primarily impact deaf and hard of hearing communities. Historically, these technologies have been developed by hearing scientists, without input from deaf researchers or community representatives. SignON was a three-year project exploring the application of SLMT, and using a co-creation approach which was led by the European Union of the Deaf. Here we describe an art-science method to engage deaf, hard of hearing, and hearing audiences with SLMT through theatre and performance. We also reflect on the insights, perspectives, and feedback shared by audience members.

Impact of face swapping and data augmentation on sign language recognition

AbstractThis study addresses the challenge of improving communication between the deaf and hearing community by exploring different sign language recognition (SLR) techniques. Due to privacy issues and the need for validation by interpreters, creating large-scale sign language (SL) datasets can be difficult. The authors address this by presenting a new Spanish isolated sign language recognition dataset, CALSE-1000, consisting of 5000 videos representing 1000 glosses, with various signers and scenarios. The study also proposes using different computer vision techniques, such as face swapping and affine transformations, to augment the SL dataset and improve the accuracy of the model I3D trained using them. The results show that the inclusion of these augmentations during training leads to an improvement in accuracy in top-1 metrics by up to 11.7 points, top-5 by up to 8.8 points and top-10 by up to 9 points. This has great potential to improve the state of the art in other datasets and other models. Furthermore, the analysis confirms the importance of facial expressions in the model by testing with a facial omission dataset and shows how face swapping can be used to include new anonymous signers without the costly and time-consuming process of recording.

Sign Language Detection Using Action Recognition LSTM Deep Learning Model

Sign language serves as a vital communication medium for the Deaf and Hard of Hearing (DHH) community, yet its recognition by computational systems remains a complex challenge. This research paper presents a novel approach to sign language detection utilizing action recognition principles through a Long Short-Term Memory (LSTM) deep learning model. Leveraging the temporal dynamics and sequential nature of sign language gestures, the LSTM model is trained to accurately identify and classify signs from video data. The proposed system processes video sequences to extract key frame features, which are then input into the LSTM network. The model's architecture is designed to capture the temporal dependencies and nuanced movements characteristic of sign language. We utilize a comprehensive dataset comprising diverse sign language gestures to train and evaluate the model. Our experimental results demonstrate that the LSTM-based approach achieves high accuracy in sign language detection, outperforming traditional static frame-based methods. The system's performance is evaluated through various metrics, including precision, recall, and F1-score, showcasing its robustness in real-world scenarios. Keywords: Gesture Recognition, Deep Learning(DL), Sign Language Recognition(SLR), TensorFlow, Matplotlib, Mediapipe, opencv-python, numpy.

SIGNSense: Auditory -Optic Impairment Communication Bridge

Language experts have recognized sign languages as natural languages with the ability to convey human emotions and ideas. Translation from written language into sign videos or extraction of spoken language sentences from sign videos is the aim of sign language translation. Sign language is the principal means of communication for the deaf and hard of hearing community, which comprises 32 million children and 328 million adults worldwide who suffer from hearing impairment. However, the inability of current systems to accurately translate and transmit sign language motions in real-time prevents effective and spontaneous communication. This research provides a revolutionary technique that enables real-time recognition of ISL gestures by integrating natural language processing with cross-modal integration. The methodology uses cutting-edge methods like the Single Shot Multibox Detector (SSD) with MobileNetV2 architecture for data collection, preprocessing, model selection, and training. In real-time inference, the trained model attains an impressive 94% accuracy rate, showcasing strong performance and encouraging outcomes for enhancing communication accessibility for people with hearing impairments

SPEECH TO SIGN CONVERSION USING NATURAL LANGUAGE PROCESSING

Speech to sign conversion is one essential piece of technology that helps the hearing community and the hearing impaired communicate. This paper presents a novel approach utilizing Natural Language Processing (NLP) techniques for speech to sign conversion. The system that is being suggested leverages advanced NLP algorithms to transcribe spoken language into text, which is thereafter converted to sign language. The process involves several key steps: speech recognition and sign language synthesis. The synthesized sign language gestures are generated based on a comprehensive database of sign language vocabulary and grammar rules. The proposed speech to sign conversion system demonstrates promising outcomes concerning accuracy, efficiency, and usability. By harnessing the power of NLP, this technology possesses the capacity to significantly enhance communication accessibility for the hearing-impaired community, facilitating seamless interaction n a number of fields, such as education and healthcare and social communication. Key Words: Speech Recognition, Natural Language Processing, Tokenization, Lemmatization, Parsing.

MLMSign: Multi-lingual multi-modal illumination-invariant sign language recognition

Sign language (SL) serves as a visual communication tool bearing great significance for deaf people to interact with others and facilitate their daily life. Wide varieties of SLs and the lack of interpretation knowledge necessitate developing automated sign language recognition (SLR) systems to attenuate the communication gap between the deaf and hearing communities. Despite numerous advanced static SLR systems, they are not practical and favorable enough for real-life scenarios once assessed simultaneously from different critical aspects: accuracy in dealing with high intra- and slight inter-class variations, robustness, computational complexity, and generalization ability. To this end, we propose a novel multi-lingual multi-modal SLR system, namely MLMSign, by taking full strengths of hand-crafted features and deep learning models to enhance the performance and the robustness of the system against illumination changes while minimizing computational cost. The RGB sign images and 2D visualizations of their hand-crafted features, i.e., Histogram of Oriented Gradients (HOG) features and a∗ channel of L∗a∗b∗ color space, are employed as three input modalities to train a novel Convolutional Neural Network (CNN). The number of layers, filters, kernel size, learning rate, and optimization technique are carefully selected through an extensive parametric study to minimize the computational cost without compromising accuracy. The system’s performance and robustness are significantly enhanced by jointly deploying the models of these three modalities through ensemble learning. The impact of each modality is optimized based on their impact coefficient determined by grid search. In addition to the comprehensive quantitative assessment, the capabilities of our proposed model and the effectiveness of ensembling over three modalities are evaluated qualitatively using the Grad-CAM visualization model. Experimental results on the test data with additional illumination changes verify the high robustness of our system in dealing with overexposed and underexposed lighting conditions. Achieving a high accuracy (>99.33%) on six benchmark datasets (i.e., Massey, Static ASL, NUS II, TSL Fingerspelling, BdSL36v1, and PSL) demonstrates that our system notably outperforms the recent state-of-the-art approaches with a minimum number of parameters and high generalization ability over complex datasets. Its promising performance for four different sign languages makes it a feasible system for multi-lingual applications.

Sign Speak: Recogninzing Sign Language with Machine Learning

Sign language serves as a critical means of communication for individuals with hearing impairments, enabling them to integrate into society effectively and express themselves. However, interpreting and recognizing sign language gestures present unique challenges due to the dynamic nature of gestures and spatial dependencies inherent in sign language communication. As a response, the SignSpeak project employs advanced machine learning techniques to address these challenges and enhance accessibility for the deaf and hard of hearing community. The project leverages a diverse dataset sourced from Kaggle, comprising images of sign language gestures captured in various contexts. The integration of advanced algorithms, such as 3D Convolutional Neural Networks (CNNs), and Gated Recurrent Units (GRUs), enables SignSpeak to recognize and interpret sign language gestures accurately and in real-time. This integration allows the model to capture both spatial and temporal features inherent in sign language, thus enabling more robust and accurate recognition. The project encompasses several critical stages, including data preprocessing, model development, training, and evaluation. Data preprocessing involves converting the image data into a suitable format and applying augmentation techniques to enhance the diversity and robustness of the dataset. Model development entails designing a deep learning architecture that combines CNNs and GRUs to effectively capture spatial and temporal dependencies in sign language gestures. Training the model involves optimizing parameters and hyperparameters to achieve optimal performance. Evaluation metrics such as accuracy, F1 score, and recall are utilized to assess the model's performance on both training and validation datasets. The trained model is then tested on a separate test dataset to evaluate its real- world performance and generalization ability. Experimental results demonstrate the efficacy of the SignSpeak approach in accurately recognizing and interpreting sign language gestures. The model achieves high accuracy scores, demonstrating its potential to enhance accessibility and inclusion for individuals with hearing impairments. By providing real-time translation of sign language into text or speech, SignSpeak contributes to breaking down communication barriers and promoting equal participation for all members of society.

DESAIN MEDIA PEMBELAJARAN BAHASA INGGRIS UNTUK SISWA TUNARUNGU BERBANTUAN TEKNOLOGI INFORMASI DAN KOMUNIKASI

Understanding words is one the language components that becoming a problem for students with hearing impairment – students with hearing and speaking problem. This difficulty exists because student with hearing impairment do have any perception about language. The difficulties of student with hearing impairment in understanding words will consequently causing problem for them in understanding and delivering sentences in written language and to interact in hearing community. The main goal of this research is to develop a media teaching and learning English based on Information Communication and Technology (ICT) and Edmodo – social network media that used exclusively as e-­‐learning tool, toward the ability of hearing impairment students in understanding meaning of words, semantic, and their interaction in learning English. The research also investigated the effectiveness of using the media in learning English for the students. The result shows that students with hearing impairment are very motivated in understanding words and sentences taught assisted with ICT and Edmodo. The everyday classroom interaction shows that the students are actively participating in individual and group works. In addition, students have a good perception about teaching assisted with ICT and Edmodo. Keyword: ICT and Edmodo, Hearing Impairment Students, Teaching and Learning English

Patient and public involvement and engagement (PPIE): how valuable and how hard? An evaluation of ALL_EARS@UoS PPIE group, 18 months on

BackgroundALL_EARS@UoS is a patient and public involvement and engagement (PPIE) group for people with lived experience of hearing loss. The purpose of the group is to share experiences of hearing loss and hearing healthcare, inform research and improve services for patients at University of Southampton Auditory Implant Service. A year after inception, we wanted to critically reflect on the value and challenges of the group. Four members of ALL_EARS@UoS were recruited to an evaluation steering group. This paper reports the evaluation of the group using the UK Standards for Public Involvement.MethodsAn anonymous, mixed-methods questionnaire was co-designed and shared with members of ALL_EARS@UoS using an online platform. The questionnaire was designed to capture satisfaction, individual feedback through free-text answers, and demographic information. Descriptive statistics have been used to express the satisfaction and demographic data. Reflexive thematic analysis has been used to analyse the free-text responses. Group engagement and activity data over time were monitored and collected.ResultsThe questionnaire response rate was 61% (11/18). Areas identified as strengths were ‘Communication’ and ‘Working together’. Five themes were developed from the thematic analysis; (1) Increased knowledge and awareness around the topic of hearing health for group members and wider society, (2) supporting research, (3) inclusivity within the group, (4) opportunity to make a difference for people in the future and (5) running of the group/group organisation. The data highlighted the value and challenges of PPIE. Members described feeling listened to and appreciation of being able to share experiences. Time of day and meeting format were identified as challenges as they affected who could attend the meetings. The ability to secure and maintain sufficient funding and time to support inclusive and diverse PPIE activities is a challenge for researchers.ConclusionsWe have identified how PPIE added value to both group members and researchers, emphasising the true benefit of PPIE. We have highlighted challenges we are facing and our plan to tackle these. We aim to continue to develop and sustain a group that reflects the diversity of the Deaf/deaf or hard of hearing community and of our local community.

Two-Way Sign Language Translator for Visually Impaired People

Hearing disabilities affect people all over the world. Nearly 360 million people have a disabling hearing loss. In the recent years, there has been a rapid increase in the number of deaf and dumb victims due to birth defects, accidents and oral diseases. Since deaf and dumb people cannot communicate with a hearing person, they have to depend on some sort of visual communication. Sign Language is not understood by the majority of hearing people. The objective of our project is to form a bridge between the hearing-impaired community and the hearing community; to further commence a two-way communication. We propose a two-way sign language translator that converts Voice to sign language and vice versa in real time. We process the video frame by frame using the OpenCV Library in Python. Furthermore, we use background subtraction method called Gaussian Mixture-based Background/Foreground Segmentation Algorithm on each frame to subtract the background. Contours of this processed image are then passed into a Deep Neural Net (DNN) to classify the frame to the corresponding written language equivalent words. For conversion from sign language to voice, we use basic Natural Language Processing and gtts to accurately conserve the grammar in the sign language. The enrolment rate and literacy among the hearing-impaired children is far below the average for the population at large. This technology will then help the normal schools better integrate the hearing-impaired community thus making education more accessible and cheaper for them. Speech Emotion Recognition (SER) is a vital aspect of human-computer interaction and emotional intelligence applications. This SER that employs Mel- frequency cepstral coefficients (MFCC) as the basis for feature extraction, followed by a Convolutional Neural Network (CNN) with a customized architecture for emotion classification. The methodology 2 involves transforming audio samples into MFCC images, enabling the application of CNNs typically used in computer vision tasks. This fusion of traditional audio analysis with deep learning techniques harnesses the power of MFCC's spectral representation and CNN's spatial pattern recognition. By modifying the CNN architecture, the model can better discern emotional cues in the MFCC images, enhancing SER performance. This strategy offers SER a viable path forward, with the ability to identify complex emotional expressions. In order to increase the accuracy of emotion recognition, it tackles the requirement for feature-rich representations and sophisticated modelling techniques. KEYWORDS: OpenCV, DNN, SER, CNN, MFCC

The Crossover: Integrating Indian Sign Language with the Mudras in an Indian Classical Dance Performance Creation

Indian classical dance uses a variety of non-verbal communication devices, with gestures called hasta or mudra being one of the most ancient sign languages powerful to transmit the meaning of the lyrics through dance. The classical dance forms of Kerala have an elaborate scheme of gestures drawn from the ancient text on aesthetic sign language, called Hastalakṣanadīpika, inspired by the Vedic, Nāṭya and Tantric traditions. The following is an attempt to document a performance-oriented, socially inclusive effort that makes Indian traditional dance performance discernible to the Deaf and Hard of Hearing (DHH) community, a disparate group that is hardly considered as a spectator during performance creation by mainstream Indian classical dancers. The Indian Sign Language (ISL) of individuals with hearing impairments is elaborate, yet is rarely used as a gestural language along with other aesthetic gestural sign languages to disseminate a narrative in a work of art. In her role as both creator and performer, Methil Devika creates work in Mohiniyāṭṭam, a graceful dance form of Kerala, integrating the sign language of those with hearing impairments along with the gestural language prescribed in the ancient treatise Hastalakṣanadīpika. She applies it strategically in her work of art, as an interlinking tool between the art and the audience. In the first few moments, “all walls came crumbling down,” something the performer quotes in the visual documentation of the work. Any separation between the hearing impaired and the artiste, as well as the spectators, become inconsequential. The performance and the presentation have been video documented in a film called The Crossover, and the following is an attempt to describe the creative process.

An Expert System for Indian Sign Language Recognition Using Spatial Attention–based Feature and Temporal Feature

Sign Language (SL) is the only means of communication for the hearing-impaired people. Normal people have difficulty understanding SL, resulting in a communication barrier between hearing impaired people and hearing community. However, the Sign Language Recognition System (SLRS) has helped to bridge the communication gap. Many SLRs are proposed for recognizing SL; however, a limited number of works are reported for Indian Sign Language (ISL). Most of the existing SLRS focus on global features other than the Region of Interest (ROI). Focusing more on the hand region and extracting local features from the ROI improves system accuracy. The attention mechanism is a widely used technique for emphasizing the ROI. However, only a few SLRS used the attention method. They employed the Convolution Block Attention Module and temporal attention but Spatial Attention (SA) is not utilized in previous SLRS. Therefore, a novel SA based SLRS named Spatial Attention-based Sign Language Recognition Module (SASLRM) is proposed to recognize ISL words for emergency situations. SASLRM recognizes ISL words by combining convolution features from a pretrained VGG-19 model and attention features from a SA module. The proposed model accomplished an average accuracy of 95.627% on the ISL dataset. The proposed SASLRM is further validated on LSA64, WLASL, and Cambridge Hand Gesture Recognition datasets where, the proposed model reached an accuracy of 97.84%, 98.86%, and 98.22%, respectively. The results indicate the effectiveness of the proposed SLRS in comparison with the existing SLRS.

Frequency and Spatial Domain-Based Approaches for Recognition of Indian Sign Language Gestures

Objectives: The objective of this paper is to introduce and demonstrate an innovative approach for the recognition of Indian sign language gestures, with a focus on bridging communication gap between the deaf and hearing communities. The goal is to contribute to the development of effective tools and technologies that facilitate seamless communication between individuals using sign language and the people with no knowledge about sign language. Methods: The methodology consists of three key steps. First, data pre-processing involves resizing and contours extraction. Next, feature extraction employs Fourier descriptors for frequency domain analysis and gray-level-co-occurrence matrix for spatial domain analysis. Finally, various machine learning models including SVM, Random Forest, Logistic Regression, K-Nearest Neighbor and Naive Bayes are trained on a standard dataset. Findings: In our controlled experimental setup, we applied a diverse set of machine learning classifiers to evaluate the proposed approach for gesture recognition. Among the classifiers tested, K-Nearest Neighbors demonstrated the highest accuracy, achieving 99.82%. To validate the robustness of our approach, we employed k-fold cross-validation with 5 folds. Novelty: This study presents an innovative method for sign language recognition by employing a dual-domain fusion strategy that prominently emphasizes the frequency domain. Through the integration of Fourier descriptors, the research conducts a detailed frequency domain analysis to characterize the contour shapes of sign language gestures. The synergy with gray-level co-occurrence matrix texture features in the spatial domain analysis, contributes to the creation of a comprehensive feature vector. The proposed approach ensures a thorough exploration of gesture features, there by advancing the precision and efficacy of sign language recognition. Keywords: Indian Sign Language (ISL), Sign Language Recognition (SLR), Frequency domain, Spatial domain, Fourier descriptors, Gray level co­occurrence matrix (GLCM), K­ Fold

Police Interactions With the Deaf and Hard of Hearing Community: Abuse, Audism, and Accessibility

Police brutality is a salient issue in recent years, especially in the United States. Black, Indigenous, and People of Color (BIPOC), as well as disabled people, experience higher rates of police violence than white and nondisabled individuals, and it is important to recognize and scrutinize this pattern of injustice. This project investigated police interactions with members of the D/deaf and Hard of Hearing (Deaf/HH) community, and analyzed the intersection of race and disability in the context of policing. Four Deaf/HH individuals were surveyed, and twenty anecdotal accounts from social media were analyzed. Research findings indicate that the Deaf/HH community faces unique challenges when interacting with police. These include difficulties communicating hearing status, misinterpretation of Deaf/HH communication methods, the formulation of strategies for safely navigating police encounters, the intersection of race and disability, and accessibility concerns. The consequences of these issues can be dire, often deadly, and thus potential solutions are discussed to help mitigate future harm.