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Predicting Group Performances Using a Personality Composite-Network Architecture During Collaborative Task
Abstract
Personality has not only been studied at an individual level, its composite effect between team members has also been indicated to be related to the overall group performance. In this work, we propose a Personality Composite-Network (PCompN) architecture that models the group-level personality composition with its intertwining effect being integrated into the network modeling of team members vocal behaviors in order to predict the group performances during collaborative problem solving tasks. In specific, we evaluate our proposed P-CompN in a large-scale dataset consist of three-person small group interactions. Our framework achieves a promising group performance classification accuracy of 70.0%, which outperforms baseline model of using only vocal behaviors without personality attributes by 14.4% absolutely. Our analysis further indicates that our proposed personality composite network impacts the vocal behavior models more significantly on the high performing groups versus the low performing groups.
Figures
A complete schematic ofour Personality Composite-Network (P-CompN). It includes an Interlocutor Acoustic Network (IAN) and a Personality Network (PN) with a decision-level fusion for the classification task. Specifically, we propose to learn a personality composite control weight to modify the original BLSTMs attention mechanism that models the effect ofgroup-level personality attributes on participants acoustic behaviors jointly.
A complete schematic ofour Personality Composite-Network (P-CompN). It includes an Interlocutor Acoustic Network (IAN) and a Personality Network (PN) with a decision-level fusion for the classification task. Specifically, we propose to learn a personality composite control weight to modify the original BLSTMs attention mechanism that models the effect ofgroup-level personality attributes on participants acoustic behaviors jointly.
Keywords
group interaction | personality traits | attention mechanism | social signal processing
Authors
Yun-Shao Lin Chun-Min Chang Chi-Chun Lee
Publication Date
2019/09/15
Conference
Interspeech
Interspeech 2019
DOI
10.21437/Interspeech.2019-2087
Publisher
ISCA