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Found 7 papers, 1 papers with code
How Can Large Language Models Enable Better Socially Assistive Human-Robot Interaction: A Brief Survey
Socially assistive robots (SARs) have shown great success in providing personalized cognitive-affective support for user populations with special needs such as older adults, children with autism spectrum disorder (ASD), and individuals with mental health challenges.
Expanding the Role of Affective Phenomena in Multimodal Interaction Research
We find that this body of research has primarily focused on enabling machines to recognize and express affect and emotion.
Towards Intercultural Affect Recognition: Audio-Visual Affect Recognition in the Wild Across Six Cultures
In our multicultural world, affect-aware AI systems that support humans need the ability to perceive affect across variations in emotion expression patterns across cultures.
Causal Discovery
Cultural Vocal Bursts Intensity Prediction
+1
Affect-Aware Deep Belief Network Representations for Multimodal Unsupervised Deception Detection
Our results motivate future work on unsupervised, affect-aware computational approaches for detecting deception and other social behaviors in the wild.
Modeling User Empathy Elicited by a Robot Storyteller
Our research informs and motivates future development of empathy perception models that can be leveraged by virtual and robotic agents during human-machine interactions.
Toward Automated Generation of Affective Gestures from Text:A Theory-Driven Approach
Communication in both human-human and human-robot interac-tion (HRI) contexts consists of verbal (speech-based) and non-verbal(facial expressions, eye gaze, gesture, body pose, etc.)
Sentiment Analysis
Robotics
Unsupervised Audio-Visual Subspace Alignment for High-Stakes Deception Detection
Our subspace-alignment (SA) approach adapts audio-visual representations of deception in lab-controlled low-stakes scenarios to detect deception in real-world, high-stakes situations.
