A tempo-relational neural architecture jointly models temporal and relational aspects of team interactions to outperform prior approaches on team performance prediction and enable efficient multi-task prediction of team constructs.
Analyzing Verbal and Nonverbal Features for Predicting Group Performance
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abstract
This work analyzes the efficacy of verbal and nonverbal features of group conversation for the task of automatic prediction of group task performance. We describe a new publicly available survival task dataset that was collected and annotated to facilitate this prediction task. In these experiments, the new dataset is merged with an existing survival task dataset, allowing us to compare feature sets on a much larger amount of data than has been used in recent related work. This work is also distinct from related research on social signal processing (SSP) in that we compare verbal and nonverbal features, whereas SSP is almost exclusively concerned with nonverbal aspects of social interaction. A key finding is that nonverbal features from the speech signal are extremely effective for this task, even on their own. However, the most effective individual features are verbal features, and we highlight the most important ones.
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Nonverbal speech features effectively predict group task performance even alone, though the strongest individual predictors are verbal features, demonstrated on merged survival task datasets.
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Boosting Team Modeling through Tempo-Relational Representation Learning
A tempo-relational neural architecture jointly models temporal and relational aspects of team interactions to outperform prior approaches on team performance prediction and enable efficient multi-task prediction of team constructs.
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Analyzing Verbal and Nonverbal Features for Predicting Group Performance
Nonverbal speech features effectively predict group task performance even alone, though the strongest individual predictors are verbal features, demonstrated on merged survival task datasets.