CONTEMPORARY MULTIMODAL DATA COLLECTION METHODOLOGY FOR RELIABLE INFERENCE OF AUTHENTIC SURPRISE
CONTEMPORARY MULTIMODAL DATA COLLECTION METHODOLOGY FOR RELIABLE INFERENCE OF AUTHENTIC SURPRISE
复制标题
用于可靠推断真实惊喜的当代多模式数据收集方法
DOI：
10.1109/wnyipw.2018.8576380
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发表时间：
2018
期刊：
影响因子：
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通讯作者：
Reynold J. Bailey
中科院分区：
文献类型：
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作者：
Jordan Edward Shea;Cecilia Ovesdotter Alm;Reynold J. Bailey
对能够理解和传达人类情感表达的智能系统的需求正变得越来越普遍。不幸的是，大多数用于开发此类系统的数据集依赖于表演或夸张的情绪，或利用从可能不可靠的来源获得的主观标签。本文报道了一种创新的数据收集方法，用于捕获真实惊喜的多模态人类信号。我们引入了两个任务，一个主持人，以引起真正的惊喜反应，同时共同收集数据，从三个人类模式：语音，面部表情和皮肤电反应。我们的工作突出了生物物理测量为基础的验证，使可靠的推理方法的潜力。一个案例研究提供了随机森林分类的基线结果。使用从这三种模式中收集的特征，我们的基线系统能够识别意外情况，与平衡数据集上的随机分配相比，准确性绝对提高了约20%。
The need for intelligent systems that can understand and convey human emotional expression is becoming increasingly prevalent. Unfortunately, most datasets for developing such systems rely on acted or exaggerated emotions, or utilize subjective labels obtained from possibly unreliable sources. This paper reports on an innovative data collection methodology for capturing multimodal human signals of authentic surprise. We introduce two tasks with a facilitator to elicit genuine reactions of surprise while co-collecting data from three human modalities: speech, facial expressions, and galvanic skin response. Our work highlights the methodological potential of biophysical measurement-based validation for enabling reliable inference. A case study is presented which provides baseline results for Random Forest classification. Using features gathered from the three modalities, our baseline system is able to identify surprise instances with approximately 20% absolute increase in accuracy compared to random assignment on a balanced dataset.