MuSE: a Multimodal Dataset of Stressed Emotion

Mimansa Jaiswal, Cristian-Paul Bara, Yuanhang Luo, Mihai Burzo, Rada Mihalcea, Emily Mower Provost


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.
Anthology ID:
2020.lrec-1.187
Volume:
Proceedings of the Twelfth Language Resources and Evaluation Conference
Month:
May
Year:
2020
Address:
Marseille, France
Editors:
Nicoletta Calzolari, Frédéric Béchet, Philippe Blache, Khalid Choukri, Christopher Cieri, Thierry Declerck, Sara Goggi, Hitoshi Isahara, Bente Maegaard, Joseph Mariani, Hélène Mazo, Asuncion Moreno, Jan Odijk, Stelios Piperidis
Venue:
LREC
SIG:
Publisher:
European Language Resources Association
Note:
Pages:
1499–1510
Language:
English
URL:
https://aclanthology.org/2020.lrec-1.187
DOI:
Bibkey:
Cite (ACL):
Mimansa Jaiswal, Cristian-Paul Bara, Yuanhang Luo, Mihai Burzo, Rada Mihalcea, and Emily Mower Provost. 2020. MuSE: a Multimodal Dataset of Stressed Emotion. In Proceedings of the Twelfth Language Resources and Evaluation Conference, pages 1499–1510, Marseille, France. European Language Resources Association.
Cite (Informal):
MuSE: a Multimodal Dataset of Stressed Emotion (Jaiswal et al., LREC 2020)
Copy Citation:
PDF:
https://aclanthology.org/2020.lrec-1.187.pdf