Lin, Yun ShaoYun ShaoLinYI-CHING LIULee, Chi ChunChi ChunLee2023-08-282023-08-282023-02-0615516857https://scholars.lib.ntu.edu.tw/handle/123456789/634773A small group is a fundamental interaction unit for achieving a shared goal. Group performance can be automatically predicted using computational methods to analyze members' verbal behavior in task-oriented interactions, as has been proven in several recent works. Most of the prior works focus on lower-level verbal behaviors, such as acoustics and turn-taking patterns, using either hand-crafted features or even advanced end-to-end methods. However, higher-level group-based communicative functions used between group members during conversations have not yet been considered. In this work, we propose a two-stage training framework that effectively integrates the communication function, as defined using Bales's interaction process analysis (IPA) coding system, with the embedding learned from the low-level features in order to improve the group performance prediction. Our result shows a significant improvement compared to the state-of-the-art methods (4.241 MSE and 0.341 Pearson's correlation on NTUBA-task1 and 3.794 MSE and 0.291 Pearson's correlation on NTUBA-task2) on the National Taiwan University Business Administration (NTUBA) small-group interaction database. Furthermore, based on the design of IPA, our computational framework can provide a time-grained analysis of the group communication process and interpret the beneficial communicative behaviors for achieving better group performance.encommunicative functions | multimodal behaviors | Small group interaction | Supervised Auto-encoder[SDGs]SDG4journal article10.1145/35587682-s2.0-85163890975https://api.elsevier.com/content/abstract/scopus_id/85163890975