Name
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/fg.2019.8756577 | 2019 14TH IEEE INTERNATIONAL CONFERENCE ON AUTOMATIC FACE AND GESTURE RECOGNITION (FG 2019) |
Field | DocType | Volume |
Computer science,Concordance correlation coefficient,Natural language processing,Artificial intelligence,Test set | Journal | abs/1812.04891 |
ISSN | Citations | PageRank |
2326-5396 | 1 | 0.35 |
References | Authors | |
11 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zong Xuan Tan | 1 | 1 | 0.35 |
| Arushi Goel | 2 | 4 | 1.41 |
| Thanh-Son Nguyen | 3 | 1 | 0.35 |
| Desmond Ong | 4 | 10 | 5.23 |