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Background - Social anxiety (SA) is a common and debilitating condition,negatively affecting life quality even at sub-diagnostic thresholds. We soughtto characterize SA's acoustic signature using hypothesis-testing and machinelearning (ML) approaches. Methods - Participants formed spontaneous utterancesresponding to instructions to refuse or consent to commands of alleged peers.Vocal properties (e.g., intensity and duration) of these utterances wereanalyzed. Results - Our prediction that, as compared to low-SA (n=31), high-SA(n=32) individuals exhibit a less confident vocal speech signature, especiallywith respect to refusal utterances, was only partially supported by theclassical hypothesis-testing approach. However, the results of the ML analysesand specifically the decision tree classifier were consistent with such speechpatterns in SA. Using a Gaussian Process (GP) classifier, we were able todistinguish between high- and low-SA individuals with high (75.6%) accuracy andgood (.83 AUC) separability. We also expected and found that vocal propertiesdifferentiated between refusal and consent utterances. Conclusions - Ourfindings provide further support for the usefulness of ML approach for thestudy of psychopathology, highlighting the utility of developing automatictechniques to create behavioral markers of SAD. Clinically, the simplicity andaccessibility of these procedures may encourage people to seek professionalhelp.

The Vocal Signature of Social Anxiety: Exploration using Hypothesis-Testing and Machine-Learning Approaches