Anna-Maria Velentza

Zhonghao Shi, Han Chen, Anna-Maria Velentza et al.

Mindfulness-based therapies have been shown to be effective in improving mental health, and technology-based methods have the potential to expand the accessibility of these therapies. To enable real-time personalized content generation for mindfulness practice in these methods, high-quality computer-synthesized text-to-speech (TTS) voices are needed to provide verbal guidance and respond to user performance and preferences. However, the user-perceived quality of state-of-the-art TTS voices has not yet been evaluated for administering mindfulness meditation, which requires emotional expressiveness. In addition, work has not yet been done to study the effect of physical embodiment and personalization on the user-perceived quality of TTS voices for mindfulness. To that end, we designed a two-phase human subject study. In Phase 1, an online Mechanical Turk between-subject study (N=471) evaluated 3 (feminine, masculine, child-like) state-of-the-art TTS voices with 2 (feminine, masculine) human therapists' voices in 3 different physical embodiment settings (no agent, conversational agent, socially assistive robot) with remote participants. Building on findings from Phase 1, in Phase 2, an in-person within-subject study (N=94), we used a novel framework we developed for personalizing TTS voices based on user preferences, and evaluated user-perceived quality compared to best-rated non-personalized voices from Phase 1. We found that the best-rated human voice was perceived better than all TTS voices; the emotional expressiveness and naturalness of TTS voices were poorly rated, while users were satisfied with the clarity of TTS voices. Surprisingly, by allowing users to fine-tune TTS voice features, the user-personalized TTS voices could perform almost as well as human voices, suggesting user personalization could be a simple and very effective tool to improve user-perceived quality of TTS voice.

Anna-Maria Velentza, Konstantina Nikou, Anne-Gwenn Bosser et al.

Socially assistive robots (SARs) are increasingly deployed in educational and information-sharing contexts, supported by advances in large language models that enable fluent real-time interaction. Despite the growing diversity of robot embodiments, it remains unclear whether a single robot appearance is appropriate across different interaction tasks or whether trust depends primarily on contextual factors. In this study, we examine how robot appearance and task type jointly influence trust in robots. Using a within-subjects video-based experiment (N = 81), participants evaluated three robots with distinct appearances while performing three educationally relevant tasks: teaching, procedural instruction, and personal-information discussion. Results from repeated-measures analyses show a strong main effect of task on trust, with participants reporting the highest trust during instructional guidance, moderate trust during teaching activities, and significantly lower trust when robots requested personal information. In contrast, robot appearance showed no significant main effect, and the interaction between appearance and task was marginal. These findings suggest that trust in human-robot interaction is shaped more strongly by task context than by physical embodiment alone. By focusing on future educators as end users, this work contributes empirical evidence toward task-aware robot deployment in educational environments and highlights the importance of aligning robot roles and behaviors with interaction goals rather than relying solely on anthropomorphic design.

Anna-Maria Velentza, Anne-Gwenn Bosser

Humor plays a central role in human social relationships, and recent advances in computational humor create new opportunities for integrating humor into human-robot interaction (HRI). While large language models (LLMs) can generate diverse forms of humor, it remains unclear how humor style, joke content, and language preference shape perceptions of robot-delivered humor in group settings. In this exploratory study, we employed a mixed factorial design in which participants evaluated AI-generated jokes delivered by a robot in a university classroom. We examined the effects of humor type (Affiliative, Self-Enhancing, Aggressive, Self-Defeating) and joke content (person-related vs. political) on perceived funniness and appropriateness, as well as preferred language. Results show that humor type significantly influences funniness, with Aggressive and Affiliative humor rated higher, while joke content primarily affects appropriateness, with person-related jokes preferred over political ones. Language preference was shaped by both joke content and participants' self-reported fluency and humor practices.