Estimating the quality of group discussions based on ensemble weakly supervised learning

Abstract

In this paper, we propose a method to improve the accuracy of a model for estimating the quality of group performance using multi-modal features. We use the group meeting corpus MATRICS, which contains the features of prosody, facial expression, language, and speech turn observed in a total of 56 group meetings. To solve the problem that not all features of all frames and modalities in the time series data are effective for estimating the labels, we propose N-teaching model that is a more robust extension of the weakly supervised co-teaching model for noise labels. In this paper, we propose N-teaching model that is a more robust extension of co-teaching. We also analyze the samples that were not used for training as noise, and compare our results with those of previous studies. We obtained the highest accuracy of MAE 0.309 in the index of Originally (novelty) of the discussion content.