Zara Returns: Improved Personality Induction and Adaptation by an Empathetic Virtual Agent

Abstract

Virtual agents need to adapt their personality to the user in order to become more empathetic. To this end, we developed Zara the Supergirl, an interactive empathetic agent, using a modular approach. In this paper, we describe the enhanced personality module with improved recognition from speech and text using deep learning frameworks. From raw audio, an average F-score of 69.6 was obtained from realtime personality assessment using a Convolutional Neural Network (CNN) model. From text, we improved personality recognition results with a CNN model on top of pre-trained word embeddings and obtained an average F-score of 71.0. Results from our Human-Agent Interaction study confirmed our assumption that people have different agent personality preferences. We use insights from this study to adapt our agent to user personality.