Using Interlocutor-Modulated Attention BLSTM to Predict Personality Traits in Small Group Interaction

Abstract

Small group interaction occurs often in workplace and education settings. Its dynamic progression is an essential factor in dictating the final group performance outcomes. The personality of each individual within the group is reflected in his/her interpersonal behaviors with other members of the group as they engage in these task-oriented interactions. In this work, we propose an interlocutor-modulated attention BSLTM (IM-aBLSTM) architecture that models an individual's vocal behaviors during small group interactions in order to automatically infer his/her personality traits. The interlocutor-modulated attention mechanism jointly optimize the relevant interpersonal vocal behaviors of other members of group during interactions. In specifics, we evaluate our proposed IM-aBLSTM in one of the largest small group interaction database, the ELEA corpus. Our framework achieves a promising unweighted recall accuracy of 87.9% in ten different binary personality trait prediction tasks, which outperforms the best results previously reported on the same database by 10.4% absolute. Finally, by analyzing the interpersonal vocal behaviors in the region of high attention weights, we observe several distinct intra- and inter-personal vocal behavior patterns that vary as a function of personality traits.