Detection of Student Teacher's Intention using Multimodal Features in a Virtual Classroom

Masato Fukuda, Hung-Hsuan Huang, Toyoaki Nishida
<span title="">2019</span> <i title="SCITEPRESS - Science and Technology Publications"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/rrqbmymjsrc75j6bbyyyprde5e" style="color: black;">Proceedings of the 11th International Conference on Agents and Artificial Intelligence</a> </i> &nbsp;
The training program for high school teachers in Japan has less opportunity to practice teaching skills. As a new practice platform, we are running a project to develop a simulation platform of school environment with computer graphics animated virtual students for students' teachers. In order to interact with virtual students and teachers, it is necessary to estimate the intention of the teacher's behavior and utterance. However, it is difficult to detection the teacher's intention at the
more &raquo; ... room only by verbal information, such as whether to ask for a response or seek a response. In this paper, we propose an automatic detection model of teacher's intention using multimodal features including linguistic, prosodic, and gestural features. For the linguistic features, we consider the models with and without lecture contents specific information. As a result, it became clear that estimating the intention of the teacher is better when using prosodic / non-verbal information together than using only verbal information. Also, the models with contents specific information perform better.
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.5220/0007379901700177">doi:10.5220/0007379901700177</a> <a target="_blank" rel="external noopener" href="https://dblp.org/rec/conf/icaart/FukudaHN19.html">dblp:conf/icaart/FukudaHN19</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/yu7sujn4e5bvlm7lsi42zgj344">fatcat:yu7sujn4e5bvlm7lsi42zgj344</a> </span>
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