Please use this identifier to cite or link to this item: https://doi.org/10.1109/FG.2019.8756577
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dc.titleA multimodal LSTM for predicting listener empathic responses over time
dc.contributor.authorTan, ZX
dc.contributor.authorGoel, A
dc.contributor.authorNguyen, TS
dc.contributor.authorOng, DC
dc.date.accessioned2020-08-05T08:16:17Z
dc.date.available2020-08-05T08:16:17Z
dc.date.issued2019-05-01
dc.identifier.citationTan, ZX, Goel, A, Nguyen, TS, Ong, DC (2019-05-01). A multimodal LSTM for predicting listener empathic responses over time. 2019 14th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2019). ScholarBank@NUS Repository. https://doi.org/10.1109/FG.2019.8756577
dc.identifier.isbn9781728100890
dc.identifier.urihttps://scholarbank.nus.edu.sg/handle/10635/171917
dc.description.abstract© 2019 IEEE. People naturally understand the emotions of-and often also empathize with-those around them. In this paper, we predict the emotional valence of an empathic listener over time as they listen to a speaker narrating a life story. We use the dataset provided by the OMG-Empathy Prediction Challenge, a workshop held in conjunction with IEEE FG 2019. We present a multimodal LSTM model with feature-level fusion and local attention that predicts empathic responses from audio, text, and visual features. Our best-performing model, which used only the audio and text features, achieved a concordance correlation coefficient (CCC) of. 29 and. 32 on the Validation set for the Generalized and Personalized track respectively, and achieved a CCC of .14 and .14 on the held-out Test set. We discuss the difficulties faced and the lessons learnt tackling this challenge.
dc.publisherIEEE
dc.sourceElements
dc.subjectcs.CL
dc.subjectcs.CL
dc.typeConference Paper
dc.date.updated2020-08-05T07:29:04Z
dc.contributor.departmentDEPARTMENT OF COMPUTER SCIENCE
dc.contributor.departmentDEPARTMENT OF INFORMATION SYSTEMS AND ANALYTICS
dc.contributor.departmentMECHANICAL ENGINEERING
dc.description.doi10.1109/FG.2019.8756577
dc.description.sourcetitle2019 14th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2019)
dc.published.statePublished
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