Predicting Emotion Perception Across Domains: A Study of Singing and Speaking

Emotion affects our understanding of the opinions and sentiments of others. Research has demonstrated that humans are able to recognize emotions in various domains, including speech and music, and that there are potential shared features that shape the emotion in both domains. In this paper, we investigate acoustic and visual features that are relevant to emotion perception in the domains of singing and speaking. We train regression models using two paradigms: (1) within-domain, in which models are trained and tested on the same domain and (2) cross-domain, in which models are trained on one domain and tested on the other domain. This strategy allows us to analyze the similarities and differences underlying the relationship between audio-visual feature expression and emotion perception and how this relationship is affected by domain of expression. We use kernel density estimation to model emotion as a probability distribution over the perception associated with multiple evaluators on ...

• Type: Journal
• Year: 2015
• Where: AAAI
• Authors: Biqiao Zhang, Emily Mower Provost, Robert Swedberg, Georg Essl
• Keywords: AAAI 2015, Intelligent Agents