Virtual Home Assistants for People with Dysarthria

This research aims to describe strategies for handling children with speech impairments at PAUD Aceh Flexi School. Speech-impaired children are part of Children with Special Needs (ABK) who experience obstacles in verbal communication, so they require a special learning approach and support. This research uses a qualitative approach with a case study method. Data was collected through observation, interviews and documentation, with the research subjects consisting of a teacher and one speech-impaired child. The research results show that treatment is carried out through three main stages: initial identification, implementation of adaptive learning strategies, and parental involvement. Learning strategies include the use of visual media, sign language, and a total communication approach that helps children express themselves. Teachers also form collaborative communications with parents to strengthen stimulation at home. However, there are obstacles such as limited communication aids, lack of teacher training, and lack of parental awareness. This research emphasizes the importance of a collaborative, communicative and empathetic approach in inclusive education to optimize the potential of children with speech impairments from an early age.

Interactive agents are becoming increasingly common in many application domains, such as education, healthcare and personal assistance. The success of such embodied agents relies on their ability to have sustained engagement with their human users. Such engagement requires agents to be socially intelligent, equipped with the ability to understand and reciprocate both verbal and non-verbal cues. While there has been tremendous progress in verbal communication, mostly driven by the success of speech recognition and question-answering, teaching agents to appropriately react to facial expressions has received less attention. In this paper, we focus on non-verbal facial cues for face-to-face communication between a user and an embodied agent. We propose a method that automatically learns to update the agent's facial expressions based on the user's expressions. We adopt a learning scheme and train a deep neural network on hundreds of videos, containing pairs of people engaging in a conversation, and without external human supervision. Our experimental results show the efficacy of our model in sustained long-term prediction of the agent's facial landmarks. We present comparative results showing that our model significantly outperforms baseline approaches and provide insightful human studies to better understand our model's qualitative performance. We release our dataset to further encourage research in this field.

Onboard virtual assistants with the ability to converse with users are gaining favour in supporting effective human-machine interaction to meet safe standards of operation in automated vehicles (AVs). Previous studies have highlighted the need to communicate situation information to effectively support the transfer of control and responsibility of the driving task. This study explores ‘interaction types’ used for this complex human-machine transaction, by analysing how situation information is conveyed and reciprocated during a transfer of control scenario. Two human drivers alternated control in a bespoke, dual controlled driving simulator with the transfer of control being entirely reliant on verbal communication. Handover dialogues were coded based on speech-act classifications, and a cluster analysis was conducted. Four interaction types were identified for both virtual assistants (i.e., agent handing over control) - Supervisor, Information Desk, Interrogator and Converser, and drivers (i.e., agent taking control) - Coordinator, Perceiver, Inquirer and Silent Receiver. Each interaction type provides a framework of characteristics that can be used to define driver requirements and implemented in the design of future virtual assistants to support the driver in maintaining and rebuilding timely situation awareness, whilst ensuring a positive user experience. This study also provides additional insight into the role of dialogue turns and takeover time and provides recommendations for future virtual assistant designs in AVs.

Speech impairment affects an individual’s ability to communicate. People affected by this problem, suffer by the communication barrier with the normal people. Hence, to express their emotions and thoughts people will use sign language for their communication and. Although sign language is common in world, there may be a difficulty for people who do not have knowledge about sign language to communicate with speech impaired people who knows the sign language. Now there is a prominent growth in the field of computer vision and deep learning, which has been tremendous development in the fields of sign and motion recognition. The proposed model focusses on overcoming the challenges in verbal communication between non-sign language speakers and sign language speakers by building a deep learning model to recognize alphabets of American sign language. The model was trained on the dataset collected from Kaggle website which consisted of 26 American Sign Language alphabets. The model achieved 99.3% mean average precision in recognition of sign and average probability value for test image achieved was 0.99. The deep learning method used to build the model achieved more accuracy than the previous solutions.

Dysarthria is a motor speech disorder resulting from impaired control of the muscles involved in speech, such as the tongue and lips. This condition affects the coordination, strength, or movement of these muscles, leading to reduced clarity, naturalness, and intelligibility in verbal communication. In recent years, deep learning (DL) techniques have become prominent in medical diagnostics, including the detection of speech disorders. DL offers robust tools for the automated analysis of speech signals, enabling earlier and more accurate identification of conditions such as dysarthria. In this study, a novel framework called EDSDCSI-DLHHS (Early Detection of Speech Disorders in Children with Speech Impairments using Deep Learning and Hybrid Heuristic Search Algorithms) is proposed. The framework combines deep learning (DL) with advanced optimization methods to enhance diagnostic accuracy. Initially, speech recordings are preprocessed to reduce noise. Then, linear prediction coding is used to extract significant speech features. A graph attention network is employed for classifying speech disorders, and its hyperparameters are fine-tuned using a hybrid optimization model combining Siberian Tiger and Sand Cat Swarm Optimization. Experimental validation using a benchmark dataset shows that the proposed model achieves 99.00% accuracy, outperforming existing approaches in detecting dysarthria in children. This framework supports early intervention and personalized treatment planning.

In recent years, communication on chat via SNS has increased by the spread of information technology. Therefore, this research aims to verify the effectiveness and possibility of communication by converging Non-verbal and Verbal communication. To support more emotional communication, we propose an emotional communication tool combining drawing that is Nonverbal communication and voice that is Verbal communication. An experiment was conducted to clarify which degree of emotions sent by the messages on chat communication via SNS. And, the system of this research was proposed based on analyzing experimental results. In the future, this system will be essential for the users to communicate happily on SNS. Here is important to use them happily regardless of the ability of drawing and voice.

<p style="text-align: justify;">Impaired communication and speech behavior is considered to be one of the key manifestations of childhood autism. At the same time speech disorders in children with autism have not been sufficiently studied. In a meta-analysis of the modern studies of autism, we present different theories explaining relations between the childhood autism and im­pairments in verbal communication; among others, we focus mainly on semantic-pragmatic and structural-functional levels of speech; spontaneous bilingualism is also discussed. The study describes in detail a case of childhood autism in a Lithuanian-speaking boy D., followed for 6 years. The uniqueness of the given case is that at the very early age D. started demonstrating a preference of English for his native language. Moreover, he learned English exclusively on the basis of media resources found on the Internet by his parents (at his request) and himself. The paper (Part 1) includes detailed information on how verbal communication is formed and data of assessments carried out by a multi­disciplinary group of experts as well as several post-testings by a psychologist and speech-language pathologist.</p>

Collaboration in a group has the potential to achieve more effective solutions for challenging problems, but collaboration per se is not an easy task, rather a stressful burden if the collaboration partners do not communicate well with each other. While Intelligent Virtual Assistants (IVAs), such as Amazon Alexa, are becoming part of our daily lives, there are increasing occurrences in which we collaborate with such IVAs for our daily tasks. Although IVAs can provide important support to users, the limited verbal interface in the current state of IVAs lacks the ability to provide effective non-verbal social cues, which is critical for improving collaborative performance and reducing task load.In this paper, we investigate the effects of IVA embodiment on collaborative decision making. In a within-subjects study, participants performed a desert survival task in three conditions: (1) performing the task alone, (2) working with a disembodied voice assistant, and (3) working with an embodied assistant. Our results show that both assistant conditions led to higher performance over when performing the task alone, but interestingly the reported task load with the embodied assistant was significantly lower than with the disembodied voice assistant. We discuss the findings with implications for effective and efficient collaborations with IVAs while also emphasizing the increased social presence and richness of the embodied assistant.

With the use of augmentative and alternative communication (AAC), people with complex communication needs could participate in social situations even if they had severe speech impairments. Even though speech-language therapists in Thailand are aware of AAC, they still mainly teach young children with complex communication needs to mainly use speech as a means to communicate. This could lead to fewer opportunities for people with complex communication needs to use AAC systems. Therefore, this article aimed to analyze and classify the items related to communication success, as a guideline to generate a screening checklist for determining whether individuals with a communication impairment would benefit from using AAC. Three previously available screening checklists pertaining to communication success were analyzed to identify the 3 domains: 1) verbal communication intelligibility, 2) verbal functional communication, and 3) verbal communication comprehension. These 3 domains were divided into 2 aspects: 1) familiar/unfamiliar communication partners, and 2) familiar/unfamiliar settings. To create a standard screening checklist that speech-language pathologists in Thailand could utilize, more research would need to be done in the future.

The patient-physician therapeutic relationship begins before the two parties’ verbal communication. For example, the patient may form an attitude from the non-verbal communication cues, such as the practice’s physical environment, the processes and the administration staff. Colors, paintings and works of art, lighting, certificates, are features that may affect the patient during his waiting time and, probably, his relationship with the physician. This research wanted to examine patients’ therapeutic experience, and, specifically, their perceptions on the ambient environment of physicians’ waiting areas and practices and their relationship with trust and proximity to the physician. To this end, we conducted a quantitative research with 95 respondents of the psychiatric health sector. Data were analyzed through advanced statistics, using the SPSS 23 statistical package. Our findings imply that there is a relationship between the physician practice’s interior decoration and the patients’ trust and proximity. Specifically, cluster analysis grouped data in three clusters that we called as ‘Leeries’, ‘Aloofs’ and ‘Intimates’, showing variability on the variables patient’s trust and proximity to physician as well as, on variables pertaining to practice’ ambient environment. The paper discusses the findings and implications as well as the limitations and propositions for future research.KeywordsPhysician practice characteristicsPatients’ therapeutic experiencePractice interior designPatient’s trust and proximity to the physician

A method for teaching about verbal and nonverbal communication is described. The Interpersonal Perception Task (IPT) consists of a videotape of 30 brief scenes. Viewers answer interpretive questions by decoding the verbal and nonverbal cues in the scenes. Information is presented in all communication channels; several categories of interaction are represented; and for each scene, there is an objective criterion of accurate judgment. Instructional techniques using the IPT highlight the subtlety and complexity of communication cues, teach about specific cues to accuracy, demonstrate the relative importance of communication channels, and help students understand the process of interpretation.

Chapter 8 - Computer Vision for Augmentative and Alternative Communication

Political oratory is a diverse genre which effectively juxtaposes speech and non-verbal cues. Politicians from diverse backgrounds and cultures often incorporate non-verbal elements, such as hand gestures, into their speeches. These hand gestures possess high utility and universality; thus, researchers believe that hand gestures and speech work together as a unified system for production and comprehension. This research explores the complex relationship between non-verbal and verbal communication within the dynamic realm of political public speech, drawing on McNeill’s (354) Gesture theory. McNeill’s theory classifies gestures into four categories: iconic, metaphoric, beat, and deictic. Employing a qualitative approach, the study provides an in-depth analysis of Imran Khan's gestures and relationship with spoken words. Iconic and metaphoric gestures were found to reinforce spoken content, while deictic and beat gestures directed attention to specific subjects and enhanced overall communication of meaning. The study found that Imran Khan effectively employed all four types of hand gestures and demonstrated a clear correlation between these gestures and his spoken words, thereby revealing his underlying thought processes. This study confirms the intricate correlation between gestures and language, emphasising their importance in influential public speaking. This resource provides unique perspectives on effective communication tactics, emphasising the significance of non-verbal clues in global conversations.

Modern neuroimaging provides a common platform for neuroscience and related disciplines to explore the human brain, mind, and behavior. We base our review on the social shaping of the human mind and discuss various aspects of brain function related to social interaction. Despite private mental contents, people can share their understanding of the world using, beyond verbal communication, nonverbal cues such as gestures, facial expressions, and postures. The understanding of nonverbal messages is supported by the brain's mirroring systems that are shaped by individual experience. Within the organism-environment system, tight links exist between action and perception, both within an individual and between several individuals. Therefore, any comprehensive brain imaging study of the neuronal basis of social cognition requires appreciation of the situated and embodied nature of human cognition, motivating simultaneous monitoring of brain and bodily functions within a socially relevant environment. Because single-person studies alone cannot unravel the dynamic aspects of interpersonal interactions, it seems both necessary and beneficial to move towards "two-person neuroscience"; technological shortcomings and a limited conceptual framework have so far hampered such a leap. We conclude by discussing some major disorders of social interaction.

We present a demonstration of a Robot Receptionist: a situated interactive robot that can coordinate turn-taking and handle multi-party engagement and dialogue in dynamic environments, where users might enter or leave the scene at any time. We use a Furhat robot, which is highly expressive and can use verbal communication as well as non-verbal cues, such as facial expressions. The system demonstrated and described here is composed of several modules, including scene analysis, engagement policies, and a dialogue manager.