@InProceedings{jaiswal-EtAl:2020:LREC,
  author    = {Jaiswal, Mimansa  and  Bara, Cristian-Paul  and  Luo, Yuanhang  and  Burzo, Mihai  and  Mihalcea, Rada  and  Provost, Emily Mower},
  title     = {MuSE: a Multimodal Dataset of Stressed Emotion},
  booktitle      = {Proceedings of The 12th Language Resources and Evaluation Conference},
  month          = {May},
  year           = {2020},
  address        = {Marseille, France},
  publisher      = {European Language Resources Association},
  pages     = {1499--1510},
  abstract  = {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.},
  url       = {https://www.aclweb.org/anthology/2020.lrec-1.187}
}