Exploring Bio-Behavioral Signal Trajectories Of State Anxiety During Public Speaking

Public speaking anxiety (PSA) is among the top social phobias in the world. Quantifying PSA in a reliable and unobtrusive manner can lay the foundation toward personalized and inexpensive technology-based interventions. Existing work for quantifying PSA often relies on self-reported measures and statistical aggregates of bio-behavioral indices, such as physiology and speech. Such aggregated bio-behavioral indices are not able to capture time-based trajectories of PSA variation, that can be very useful for better understanding and reliably predicting moments of anxiety. We tackle this problem by introducing temporal parametric models to quantify bio-behavioral trajectories of PSA throughout a public speaking encounter. Using data from 55 participants in a real-life public speaking task, the parameters of the proposed models are found to be significantly correlated with individuals' trait characteristics of general and communication-based anxiety, outperforming aggregate mean bio-behavioral measures.