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Title: Multimodal Modeling of Task-Mediated Confusion
In order to build more human-like cognitive agents, systems capable of detecting various human emotions must be designed to respond appropriately. Confusion, the combination of an emotional and cognitive state, is under-explored. In this paper, we build upon prior work to develop models that detect confusion from three modalities: video (facial features), audio (prosodic features), and text (transcribed speech features). Our research improves the data collection process by allowing for continuous (as opposed to discrete) annotation of confusion levels. We also craft models based on recurrent neural networks (RNNs) given their ability to predict sequential data. In our experiments, we find that text and video modalities are the most important in predicting confusion while the explored audio features are relatively unimportant predictors of confusion in our data.  more » « less
Award ID(s):
2125362 1851591
NSF-PAR ID:
10343330
Author(s) / Creator(s):
; ; ; ;
Date Published:
Journal Name:
Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: Student Research Workshop
Page Range / eLocation ID:
188 to 194
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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