Automatic Classification of Handshapes in Russian Sign Language

Handshapes are one of the basic parameters of signs, and any phonological or phonetic analysis of a sign language must account for handshapes. Many sign languages have been carefully analysed by sign language linguists to create handshape inventories. This has theoretical implications, but also applied use, as an inventory is necessary for generating corpora for sign languages that can be searched, filtered, sorted by different sign components (such as handshapes, orientation, location, movement, etc.). However, creating an inventory is a very time-consuming process, thus only a handful of sign languages have them. Therefore, in this work we firstly test an unsupervised approach with the aim to automatically generate a handshape inventory. The process includes hand detection, cropping, and clustering techniques, which we apply to a commonly used resource: the Spreadthesign online dictionary (www.spreadthesign.com), in particular to Russian Sign Language (RSL). We then manually verify the data to be able to apply supervised learning to classify new data.publishedVersio

K-RSL: a Corpus for Linguistic Understanding, Visual Evaluation, and Recognition of Sign Languages

The paper presents the first dataset that aims to serve interdisciplinary purposes for the utility of computer vision community and sign language linguistics. To date, a majority of Sign Language Recognition (SLR) approaches focus on recognising sign language as a manual gesture recognition problem. However, signers use other articulators: facial expressions, head and body position and movement to convey linguistic information. Given the important role of non-manual markers, this paper proposes a dataset and presents a use case to stress the importance of including non-manual features to improve the recognition accuracy of signs. To the best of our knowledge no prior publicly available dataset exists that explicitly focuses on non-manual components responsible for the grammar of sign languages. To this end, the proposed dataset contains 28250 videos of signs of high resolution and quality, with annotation of manual and nonmanual components. We conducted a series of evaluations in order to investigate whether non-manual components would improve signs’ recognition accuracy. We release the dataset to encourage SLR researchers and help advance current progress in this area toward realtime sign language interpretation. Our dataset will be made publicly available at https:// krslproject.github.io/krsl-corpuspublishedVersio

THE EFFECTS OF THE COVID-19 PANDEMIC ON THE WELL-BEING OF CHILDREN WITH AUTISM SPECTRUM DISORDER: PARENTS’ PERSPECTIVES

The COVID-19-related lockdown interrupted children’s learning progress and discontinued social learning and regular activities that children with autism spectrum disorder (ASD) rely on socially and physically. Negative consequences for children with ASD were reported far and wide. To investigate this problem in Kazakhstan, we conducted a mixed-methods study that drew on data from an online survey with 97 parents and semistructured interviews with 14 parents. While parent-report quantitative results suggest that children were likely to experience negative impacts of the pandemic due to disrupted educational and therapeutic services, qualitative findings confirm that they have experienced an elevated mental health and behavioral challenges during the lockdown. Remote educational and therapeutic services were not helpful as families coped with pandemic-caused problems on their own. We highlight that continued support and care during and after a crisis is vital not only for children with ASD but also for the families under-resourced mentally and socially

Functional Data Analysis of Non-manual Marking of Questions in Kazakh-Russian Sign Language

This paper is a continuation of Kuznetsova et al. (2021), which described non-manual markers of polar and wh-questions in comparison with statements in an NLP dataset of Kazakh-Russian Sign Language (KRSL) using Computer Vision. One of the limitations of the previous work was the distortion of the 3D face landmarks when the head was rotated. The proposed solution was to train a simple linear regression model to predict the distortion and then subtract it from the original output. We improve this technique with a multilayer perceptron. Another limitation that we intend to address in this paper is the discrete analysis of the continuous movement of non-manuals. In Kuznetsova et al. (2021) we averaged the value of the non-manual over its scope for statistical analysis. To preserve information on the shape of the movement, in this study we use a statistical tool that is often used in speech research, Functional Data Analysis, specifically Functional PCA.publishedVersio

Evaluation of Manual and Non-manual Components for Sign Language Recognition

The motivation behind this work lies in the need to differentiate between similar signs that differ in non-manual components present in any sign. To this end, we recorded full sentences signed by five native signers and extracted 5200 isolated sign samples of twenty frequently used signs in Kazakh-Russian Sign Language (K-RSL), which have similar manual components but differ in non-manual components (i.e. facial expressions, eyebrow height, mouth, and head orientation). We conducted a series of evaluations in order to investigate whether non-manual components would improve sign’s recognition accuracy. Among standard machine learning approaches, Logistic Regression produced the best results, 78.2% of accuracy for dataset with 20 signs and 77.9% of accuracy for dataset with 2 classes (statement vs question). Dataset can be downloaded from the following website: https://krslproject.github.io/krsl20/publishedVersio

COGNITIVE LEARNING AND ROBOTICS: INNOVATIVE TEACHING FOR INCLUSIVITY

We present the interdisciplinary CoWriting Kazakh project in which a social robot acts as a peer in learning the new Kazakh Latin alphabet, to which Kazakhstan is going to shift from the current Kazakh Cyrillic by 2030. We discuss the past literature on cognitive learning and script acquisition in-depth and present a theoretical framing for this study. The results of word and letter analyses from two user studies conducted between 2019 and 2020 are presented. Learning the new alphabet through Kazakh words with two or more syllables and special native letters resulted in significant learning gains. These results suggest that reciprocal Cyrillic-to-Latin script learning results in considerable cognitive benefits due to mental conversion, word choice, and handwriting practices. Overall, this system enables school-age children to practice the new Kazakh Latin script in an engaging learning scenario. The proposed theoretical framework illuminates the understanding of teaching and learning within the multimodal robot-assisted script learning scenario and beyond its scope

FluentSigners-50: A signer independent benchmark dataset for sign language processing

This paper presents a new large-scale signer independent dataset for Kazakh-Russian Sign Language (KRSL) for the purposes of Sign Language Processing. We envision it to serve as a new benchmark dataset for performance evaluations of Continuous Sign Language Recognition (CSLR) and Translation (CSLT) tasks. The proposed FluentSigners-50 dataset consists of 173 sentences performed by 50 KRSL signers resulting in 43,250 video samples. Dataset contributors recorded videos in real-life settings on a wide variety of backgrounds using various devices such as smartphones and web cameras. Therefore, distance to the camera, camera angles and aspect ratio, video quality, and frame rates varied for each dataset contributor. Additionally, the proposed dataset contains a high degree of linguistic and inter-signer variability and thus is a better training set for recognizing a real-life sign language. FluentSigners-50 baseline is established using two state-of-the-art methods, Stochastic CSLR and TSPNet. To this end, we carefully prepared three benchmark train-test splits for models’ evaluations in terms of: signer independence, age independence, and unseen sentences. FluentSigners-50 is publicly available at https://krslproject.github.io/FluentSigners-50/publishedVersio

THE QUANTITATIVE CASE-BY-CASE ANALYSES OF THE SOCIO-EMOTIONAL OUTCOMES OF CHILDREN WITH ASD IN ROBOT-ASSISTED AUTISM THERAPY

With its focus on robot-assisted autism therapy, this paper presents case-by-case analyses of socio-emotional outcomes of 34 children aged 3–12 years old, with different cases of Autism Spectrum Disorder (ASD) and Attention Deficit Hyperactivity Disorder (ADHD).We grouped children by the following characteristics: ASD alone (n = 22), ASD+ADHD (n = 12), verbal (n = 11), non-verbal (n = 23), low-functioning autism (n = 24), and high-functioning autism (n = 10). This paper provides a series of separate quantitative analyses across the first and last sessions, adaptive and non-adaptive sessions, and parent and no-parent sessions, to present child experiences with the NAO robot, during play-based activities. The results suggest that robots are able to interact with children in social ways and influence their social behaviors over time. Each child with ASD is a unique case and needs an individualized approach to practice and learn social skills with the robot. We, finally, present specific child–robot intricacies that affect how children engage and learn over time as well as across different sessions

Ubiquitous Human Perception for Real-Time Gender Estimation

Abstract -In environments where robotic systems are deployed people often have different requirements for the robotic services and human-robot interaction methods. This paper presents a robotic system that exploits the advantages of ubiquitous perception in order to gather knowledge from multiple sensors and various modalities. This ubiquitous human perception will facilitate user profiling in order to support personalised services and individual human-robot interaction. This system combines ubiquitous smart sensing, methods of multi-modal human perception and existing human recognition algorithms from the field of biometrics to collectively work towards a real-time, robust and scalable solution for gender estimation

Lessons Learned About Designing and Conducting Studies From HRI Experts

The field of human-robot interaction (HRI) research is multidisciplinary and requires researchers to understand diverse fields including computer science, engineering, informatics, philosophy, psychology, and more disciplines. However, it is hard to be an expert in everything. To help HRI researchers develop methodological skills, especially in areas that are relatively new to them, we conducted a virtual workshop, Workshop Your Study Design (WYSD), at the 2021 International Conference on HRI. In this workshop, we grouped participants with mentors, who are experts in areas like real-world studies, empirical lab studies, questionnaire design, interview, participatory design, and statistics. During and after the workshop, participants discussed their proposed study methods, obtained feedback, and improved their work accordingly. In this paper, we present 1) Workshop attendees’ feedback about the workshop and 2) Lessons that the participants learned during their discussions with mentors. Participants’ responses about the workshop were positive, and future scholars who wish to run such a workshop can consider implementing their suggestions. The main contribution of this paper is the lessons learned section, where the workshop participants contributed to forming this section based on what participants discovered during the workshop. We organize lessons learned into themes of 1) Improving study design for HRI, 2) How to work with participants - especially children -, 3) Making the most of the study and robot’s limitations, and 4) How to collaborate well across fields as they were the areas of the papers submitted to the workshop. These themes include practical tips and guidelines to assist researchers to learn about fields of HRI research with which they have limited experience. We include specific examples, and researchers can adapt the tips and guidelines to their own areas to avoid some common mistakes and pitfalls in their research