  Public speaking and presentation competence plays an essential role in many
areas of social interaction in our educational, professional, and everyday
life. Since our intention during a speech can differ from what is actually
understood by the audience, the ability to appropriately convey our message
requires a complex set of skills. Presentation competence is cultivated in the
early school years and continuously developed over time. One approach that can
promote efficient development of presentation competence is the automated
analysis of human behavior during a speech based on visual and audio features
and machine learning. Furthermore, this analysis can be used to suggest
improvements and the development of skills related to presentation competence.
In this work, we investigate the contribution of different nonverbal behavioral
cues, namely, facial, body pose-based, and audio-related features, to estimate
presentation competence. The analyses were performed on videos of 251 students
while the automated assessment is based on manual ratings according to the
T\"ubingen Instrument for Presentation Competence (TIP). Our classification
results reached the best performance with early fusion in the same dataset
evaluation (accuracy of 71.25%) and late fusion of speech, face, and body pose
features in the cross dataset evaluation (accuracy of 78.11%). Similarly,
regression results performed the best with fusion strategies.
