Paper Info
Title
Task-independent Recognition of Communication Skills in Group Interaction Using Time-series Modeling
Abstract
AbstractCase studies of group discussions are considered an effective way to assess communication skills (CS). This method can help researchers evaluate participants’ engagement with each other in a specific realistic context. In this article, multimodal analysis was performed to estimate CS indices using a three-task-type group discussion dataset, the MATRICS corpus. The current research investigated the effectiveness of engaging both static and time-series modeling, especially in task-independent settings. This investigation aimed to understand three main points: first, the effectiveness of time-series modeling compared to nonsequential modeling; second, multimodal analysis in a task-independent setting; and third, important differences to consider when dealing with task-dependent and task-independent settings, specifically in terms of modalities and prediction models. Several modalities were extracted (e.g., acoustics, speaking turns, linguistic-related movement, dialog tags, head motions, and face feature sets) for inferring the CS indices as a regression task. Three predictive models, including support vector regression (SVR), long short-term memory (LSTM), and an enhanced time-series model (an LSTM model with a combination of static and time-series features), were taken into account in this study. Our evaluation was conducted by using the R2 score in a cross-validation scheme. The experimental results suggested that time-series modeling can improve the performance of multimodal analysis significantly in the task-dependent setting (with the best R2 = 0.797 for the total CS index), with word2vec being the most prominent feature. Unfortunately, highly context-related features did not fit well with the task-independent setting. Thus, we propose an enhanced LSTM model for dealing with task-independent settings, and we successfully obtained better performance with the enhanced model than with the conventional SVR and LSTM models (the best R2 = 0.602 for the total CS index). In other words, our study shows that a particular time-series modeling can outperform traditional nonsequential modeling for automatically estimating the CS indices of a participant in a group discussion with regard to task dependency.
Year
DOI
Venue
2021
10.1145/3450283
ACM Transactions on Multimedia Computing, Communications, and Applications
Keywords
DocType
Volume
Multimodal analysis, time-series modeling, task independent, communication skills, group discussion
Journal
17
Issue
ISSN
Citations 
4
1551-6857
0
PageRank 
References 
Authors
0.34
0
3
Name
Order
Citations
PageRank
Candy Olivia Mawalim102.03
Shogo Okada210120.10
Yukiko Nakano350162.37