Alice Baird(University of Augsburg), Nicholas Cummins(University of Augsburg), Sebastian Schnieder(Institut für experimentelle Psychophysiologie), Jarek Krajewski(Univ. Wuppertal) and Björn Schuller(University of Augsburg / Imperial College London)
The current level of global uncertainty is having an implicit effect on those with a diagnosed anxiety disorder. Anxiety can impact vocal qualities, particularly as physical symptoms of anxiety include muscle tension and shortness of breath. To this end, in this study, we explore the effect of anxiety on speech - focusing on four classes of sustained vowels (sad, smiling, comfortable, and powerful) - via feature analysis and a series of regression experiments. We extract three well-known acoustic feature sets and evaluate the efficacy of machine learning for prediction of anxiety based on the Beck Anxiety Inventory (BAI) score. Of note, utilising a support vector regressor, we find that the effects of anxiety in speech appear to be stronger at higher BAI levels. Significant differences (p<0.05) between test predictions of Low and High-BAI groupings support this. Furthermore, when utilising a High-BAI grouping for the prediction of standardised BAI, significantly higher results are obtained for smiling sustained vowels, of up to 0.646 Spearman's Correlation Coefficient (rho), and up to 0.592 rho with all sustained vowels. A significantly stronger (Cohens d of 1.718) result than all data combined without grouping, which achieves at best 0.234 rho.