MuSE: a Multimodal Dataset of Stressed Emotion
MuSE: a Multimodal Dataset of Stressed Emotion
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发表时间：
2020-05
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通讯作者：
Mimansa Jaiswal;Cristian-Paul Bara;Y. Luo;Mihai Burzo;Rada Mihalcea;E. Provost
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作者：
Mimansa Jaiswal;Cristian-Paul Bara;Y. Luo;Mihai Burzo;Rada Mihalcea;E. Provost
赋予自动代理人提供支持，娱乐和与人类互动的能力，需要感知用户的情感状态。这些情感状态受到情感诱因、当前心理状态和各种会话因素的影响。虽然在单一和二元设置的情绪分类是一个既定的领域，这些额外的因素对情绪的生产和感知的影响是研究不足。本文提出了一个新的数据集，多模态应激情绪（MuSE），研究压力的存在和情感表达之间的多模态相互作用。我们描述了数据收集协议，可能的使用领域，以及录音的情感内容的注释。本文还提出了几个基线来衡量多模态特征的情绪和压力分类的性能。
Endowing automated agents with the ability to provide support, entertainment and interaction with human beings requires sensing of the users’ affective state. These affective states are impacted by a combination of emotion inducers, current psychological state, and various conversational factors. Although emotion classification in both singular and dyadic settings is an established area, the effects of these additional factors on the production and perception of emotion is understudied. This paper presents a new dataset, Multimodal Stressed Emotion (MuSE), to study the multimodal interplay between the presence of stress and expressions of affect. We describe the data collection protocol, the possible areas of use, and the annotations for the emotional content of the recordings. The paper also presents several baselines to measure the performance of multimodal features for emotion and stress classification.