  Background - Social anxiety (SA) is a common and debilitating condition,
negatively affecting life quality even at sub-diagnostic thresholds. We sought
to characterize SA's acoustic signature using hypothesis-testing and machine
learning (ML) approaches. Methods - Participants formed spontaneous utterances
responding to instructions to refuse or consent to commands of alleged peers.
Vocal properties (e.g., intensity and duration) of these utterances were
analyzed. Results - Our prediction that, as compared to low-SA (n=31), high-SA
(n=32) individuals exhibit a less confident vocal speech signature, especially
with respect to refusal utterances, was only partially supported by the
classical hypothesis-testing approach. However, the results of the ML analyses
and specifically the decision tree classifier were consistent with such speech
patterns in SA. Using a Gaussian Process (GP) classifier, we were able to
distinguish between high- and low-SA individuals with high (75.6%) accuracy and
good (.83 AUC) separability. We also expected and found that vocal properties
differentiated between refusal and consent utterances. Conclusions - Our
findings provide further support for the usefulness of ML approach for the
study of psychopathology, highlighting the utility of developing automatic
techniques to create behavioral markers of SAD. Clinically, the simplicity and
accessibility of these procedures may encourage people to seek professional
help.
