Estimating communication skills using dialogue acts and nonverbal features in multiple discussion datasets

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.