Biological and Behavioral Information-Based Method of Predicting Listener Emotions Toward Speaker Utterances During Group Discussion
Biological and Behavioral Information-Based Method of Predicting Listener Emotions Toward Speaker Utterances During Group Discussion
复制标题
基于生物和行为信息的小组讨论期间预测听众对说话者话语情绪的方法
DOI：
10.1007/978-981-15-8944-7_12
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发表时间：
2020
期刊：
影响因子：
--
通讯作者：
Maeda Eisaku
中科院分区：
文献类型：
--
作者：
Sakai Motoki;Shuzo Masaki;Yuasa Masahide;Matsui Kanae;Maeda Eisaku
有许多类型的学习环境中提出的高等教育场所，需要发展的学习能力多样化的剧目。小组讨论（GD）就是这样一种学习环境，参与的学生需要多种沟通技巧。从技术上讲，这是可取的学生参与GD了解其他参与者的情绪反应，对他们的话语，以改善他们的措辞，内容等。本研究的目的是预测听众的情绪响应扬声器的话语使用多模态传感器。在实验中，GD进行了20名学生。六个基本的情绪被记录为反应说话人的话语在GD使用的情绪注释工具。本研究通过使用加速度计、心电图（ECG）和肌电图（EMG）来预测情绪的发生。从传感器数据中，计算了时域和频域中的56个特征，并进行了Kruskal-Wallis检验和多重比较检验，以调查所收集的特征之间是否存在显著差异。结果发现，六种基本情绪的组间差异显著（）。作为一个应用，它已被证明，消极和积极的情绪可以区分支持向量机（SVM）与76%的F1。
There are many types of learning environments presented in higher education venues, requiring the development of a diverse repertoire of learning abilities. Group discussion (GD) is one such learning environment, and the students who participate require multiple communication skills. Technically, it is desirable for a student participating in a GD to understand other participants’ emotional reactions toward their utterances to improve their locution, content, etc. The purpose of this research is to predict listeners’ emotions in response to speakers’ utterances using multimodal sensors. In experiments, GDs were conducted with 20 students. Six basic emotions were recorded as responses to speakers’ utterances during the GDs using an emotional annotation tool. This study predicted the occurrence of the emotions by using an accelerometer, an electrocardiogram (ECG), and an electromyography (EMG). From sensor data, 56 features in the time and frequency domains were calculated, and Kruskal–Wallis tests and multiple comparison tests were performed to investigate whether there were significant differences among the features collected. As a result, there were significant differences among the groups of six basic emotions (). As an application, it has been shown that negative and positive emotions could be distinguished by support vector machine (SVM) with 76% F1.
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