Can I read Learning to Generate Context-Sensitive Backchannel Smiles for Embodied AI Agents with Applications in Mental Health Dialogues on EtoBox?

Learning to Generate Context-Sensitive Backchannel Smiles for Embodied AI Agents with Applications in Mental Health Dialogues by Bilalpur, Maneesh; Inan, Mert; Zeinali, Dorsa; Cohn, Jeffrey F.; Alikhani, Malihe is a scholarly article available to read on EtoBox.

What is Learning to Generate Context-Sensitive Backchannel Smiles for Embodied AI Agents with Applications in Mental Health Dialogues about?

Addressing the critical shortage of mental health resources for effective screening, diagnosis, and treatment remains a significant challenge. This scarcity underscores the need for innovative solutions, particularly in enhancing the accessibility and efficacy of therapeutic support. Embodied agents with advanced interactive capabilities emerge as a promising and cost-effective supplement to traditional caregiving methods. Crucial to these agents' effectiveness is their ability to simulate non-verbal behaviors, like backchannels, that are pivotal in establishing rapport and understanding in therapeutic contexts but remain under-explored. To improve the rapport-building capabilities of embodied agents we annotated backchannel smiles in videos of intimate face-to-face conversations over topics such as mental health, illness, and relationships. We hypothesized that both speaker and listener behaviors affect the duration and intensity of backchannel smiles. Using cues from speech prosody and language along with the demographics of the speaker and listener, we found them to contain significant predictors of the intensity of backchannel smiles. Based on our findings, we introduce backcha

Author

Bilalpur, Maneesh; Inan, Mert; Zeinali, Dorsa; Cohn, Jeffrey F.; Alikhani, Malihe

Published

2024

Language

EN