Paper Info
Title
Estimating communication skills using dialogue acts and nonverbal features in multiple discussion datasets.
Abstract
This paper focuses on the computational analysis of the individual communication skills of participants in a group. The computational analysis was conducted using three novel aspects to tackle the problem. First, we extracted features from dialogue (dialog) act labels capturing how each participant communicates with the others. Second, the communication skills of each participant were assessed by 21 external raters with experience in human resource management to obtain reliable skill scores for each of the participants. Third, we used the MATRICS corpus, which includes three types of group discussion datasets to analyze the influence of situational variability regarding to the discussion types. We developed a regression model to infer the score for communication skill using multimodal features including linguistic and nonverbal features: prosodic, speaking turn, and head activity. The experimental results show that the multimodal fusing model with feature selection achieved the best accuracy, 0.74 in R2 of the communication skill. A feature analysis of the models revealed the task-dependent and task-independent features to contribute to the prediction performance.
Year
DOI
Venue
2016
10.1145/2993148.2993154
ICMI
Keywords
Field
DocType
Inference, Communication skills, Group conversation analysis, Social signal processing, Dialogue acts, Multiple tasks
Dialog box,Human resource management,Feature selection,Inference,Computer science,Regression analysis,Nonverbal communication,Human–computer interaction,Situational ethics,Pattern recognition (psychology)
Conference
Citations 
PageRank 
References 
7
0.54
17
Authors
7
Name
Order
Citations
PageRank
Shogo Okada110120.10
Yoshihiko Ohtake270.54
Yukiko Nakano350162.37
Yuki Hayashi43811.12
Hung-Hsuan Huang514032.60
Yutaka Takase6196.26
Katsumi Nitta716044.94