Predicting Users' Perceived Trust in Embodied Conversational Agents Using Vocal Dynamics

One of the major challenges facing neurophysiological HCI design is to determine the systems and sensors that accurately and noninvasively measure human cognitive processes. Specifically, it is a significant undertaking to integrate sensors and measurements into an information system and accurately measure and interpret the human state. Using an experimental design this study explores the use of unobtrusive sensors based on behavioral and neurophysiological responses to predict human trust using the voice. Participants (N=88) completed a face-to-face interview with an Embodied Conversational Agent (ECA) and reported their perceptions of the ECA. They reported three dimensions consistent with the Mayer model of perceived trustworthiness. During the interaction, the demeanor and gender of the avatar was manipulated and these manipulations affected the reported measures of trustworthiness. Using growth modeling and multilevel analysis of covariance methods, a model was developed that could predict human trust during the interaction using the voice, time, and demographics.