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Public speaking performances are not only characterized by the presentation of the content, but also by the presenters’ nonverbal behavior, such as gestures, tone of voice, vocal variety, and facial expressions. Within this work, we seek to identify automatic nonverbal behavior descriptors that correlate with expert-assessments of behaviors characteristic of good and bad public speaking performances. We present a novel multimodal corpus recorded with a virtual audience public speaking training platform. Lastly, we utilize the behavior descriptors to automatically approximate the overall assessment of the performance using support vector regression in a speaker-independent experiment and yield promising results approaching human performance.