Knowledge- and Data-Driven Models of Multimodal Trajectories of Public Speaking Anxiety in Real and Virtual Settings

Abstract

Public speaking skills are essential to professional success. Yet, public speaking anxiety (PSA) is considered one of the most common social phobias. Understanding PSA can help communication experts identify effective ways to treat this communication-based disorder. Existing works on PSA rely on self-reports and aggregate multimodal measures which do not capture the temporal variation in PSA. This paper examines temporal trajectories of acoustic and physiological measures throughout the public speaking encounter with real and virtual audiences, and aims to model those in both knowledge- and data-driven ways. Knowledge-driven models leverage theoretically-grounded patterns through fitting interpretable parametric functions to the corresponding signals. Data-driven models consider the functional nature of multimodal signals via functional principal component analysis. Results indicate that the parameters of the proposed models can successfully estimate individuals’ trait anxiety in both real-life and virtual reality settings, and suggest that models trained on data obtained in virtual public speaking stimuli are able to estimate levels of PSA in real-life.