MMOD-COG: A Database for Multimodal Cognitive Load Classification
MMOD-COG: A Database for Multimodal Cognitive Load Classification
复制标题
MMOD-COG：多模式认知负荷分类数据库
DOI：
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发表时间：
2019
期刊：
影响因子：
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通讯作者：
D. Petrinović
中科院分区：
文献类型：
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作者：
Igor Mijić;Marko Šarlija;D. Petrinović
本文提出了一个数据集的多模态分类的认知负荷记录样本的学生。认知负荷是通过执行基本的算术任务来诱导的，而数据集的多模态方面则以语音和生理反应的形式出现。该数据集的目标是双重的：首先是提供一种替代现有的认知负荷集中的数据集，通常基于Stroop任务或工作记忆任务;其次是以一种使响应适合语音和生理响应分析的方式实现认知负荷任务，最终使其成为多模态。本文还提出了初步的分类基准，其中SVM分类器的训练和评估，无论是语音或生理信号和两者的组合。分类器的多模态性质可能会改善这个固有的具有挑战性的机器学习问题的结果，因为它提供了更多关于认知负荷如何在情感反应中表现出来的参与者内部和参与者之间差异的数据。
This paper presents a dataset for multimodal classification of cognitive load recorded on a sample of students. The cognitive load was induced by way of performing basic arithmetic tasks, while the multimodal aspect of the dataset comes in the form of both speech and physiological responses to those tasks. The goal of the dataset was two-fold: firstly to provide an alternative to existing cognitive load focused datasets, usually based around Stroop tasks or working memory tasks; and secondly to implement the cognitive load tasks in a way that would make the responses appropriate for both speech and physiological response analysis, ultimately making it multimodal. The paper also presents preliminary classification benchmarks, in which SVM classifiers were trained and evaluated solely on either speech or physiological signals and on combinations of the two. The multimodal nature of the classifiers may provide improvements on results on this inherently challenging machine learning problem because it provides more data about both the intra-participant and inter-participant differences in how cognitive load manifests itself in affective responses.
DOI：
10.1016/j.jvoice.2016.10.021
发表时间：
2017-07
期刊：
Journal of voice : official journal of the Voice Foundation
影响因子：
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作者：
MacPherson MK;Abur D;Stepp CE
通讯作者：
Stepp CE