The increasing prevalence of voice assistants in daily life has transformed human-technology interaction. However, current voice assistants fall short in their ability to comprehend users’ emotional states through speech and respond empathetically. This limitation leads to suboptimal user experiences and missed opportunities for personalization. To address this gap, this paper presents a groundbreaking prototype of a future voice assistant equipped with the capability to recognize and respond to users’ emotional states. The paper outlines the architecture of a speech-emotion recognition system that utilizes a deep neural network incorporating a sparse attention mechanism, yielding promising results. Furthermore, the paper embarks on an initial exploration of strategies for emotion-based responses, drawing inspiration from human techniques for managing unpleasant emotions. A user study is conducted, exposing participants to emotions such as anger, sadness, and fear, while engaging the voice assistant to elicit happiness. Preliminary findings suggest a tendency toward neutral emotional responses, providing valuable insights into the potential of speech feature analysis as an alternative approach for identifying and responding to emotions.

Dementia poses significant challenges to individuals, particularly those grappling with Alzheimer’s disease, impacting their daily lives, well-being, and communication. The task of enhancing communication for People with Dementia (PwD) has grown increasingly complex, prompting the exploration of innovative solutions such as voice assistants. Understanding PwD’s speech features and conversational content can enhance their communication experiences. Speech feature analysis plays a pivotal role in this process, offering valuable insights into tailoring interactions with PwD. In this study, we analyze speech recordings from the ADReSSo database, which contains speech from both Alzheimer’s patients and healthy aging persons. We discovered that several speech features exhibited significant differences between PwD and their healthy counterparts. These speech features can help with dementia identification and also represent the PwD’s speaking patterns. Furthermore, the speech emotions of PwD and healthy aging people differ significantly. The clear differences in speech emotions offer valuable cues for crafting emotionally attuned interactions between PwD and voice assistants. These valuable information serves as the foundation for developing future personalized voice assistants dedicated to supporting individuals with dementia by incorporating the identified speech features and emotional information.

The emergence of large vision models (LVMs) is following in the footsteps of the recent prosperity of Large Language Models (LLMs) in following years. However, there’s a noticeable gap in structured research applying LVMs to human-robot interaction (HRI), despite extensive evidence supporting the efficacy of vision models in enhancing interactions between humans and robots. Recognizing the vast and anticipated potential, we introduce an initial design space that incorporates domain-specific LVMs, chosen for their superior performance over normal models. We delve into three primary dimensions: HRI contexts, vision-based tasks, and specific domains. The empirical evaluation was implemented among 15 experts across five evaluated metrics, showcasing the primary efficacy in relevant decision-making scenarios. We explore the process of ideation and potential application scenarios, envisioning this design space as a foundational guideline for future HRI system design, emphasizing accurate domain alignment and model selection.

Dementia significantly impacts cognitive, behavioral, and functional abilities, creating challenges for both individuals and caregivers. Recent advancements in HCI have introduced innovative technological solutions to support people with dementia (PwD) and their caregivers. This scoping review systematically examines 32 recent publications from leading digital libraries, categorizing technological interventions into four key domains: Assistive and Smart Technology for Daily Life, Social Interaction and Communication, Well-being and Psychological Support, and Caregiver Support and Training. Our analysis highlights how emerging technologies are transforming dementia care. These technologies enhance quality of life by promoting independence, fostering social engagement, and providing emotional and cognitive support. However, the review also identifies critical gaps, particularly in addressing the needs of individuals with early-stage dementia and the lack of individualized support mechanisms. By emphasizing user-centered design, accessibility, and ethical considerations, this paper offers a structured roadmap for future research and practice in dementia care. It bridges the gap between technological innovation and the real-world needs of PwD and their caregivers, providing valuable insights for researchers, practitioners, and policymakers. This review not only synthesizes current advancements but also sets the stage for future HCI-driven innovations in dementia care, aiming to improve outcomes for an aging global population. Findings primarily reflect older adults; implications for early-onset dementia are discussed.