Analyzing the Influence of Dataset Composition for Emotion Recognition

IROS 2018 Workshop on Language and Robotics - Oct 2018.
Associated documents : SMWW18.pdf [143Ko]  
Recognizing emotions from text in multimodal architectures has yielded promising results, surpassing video and audio modalities under certain circumstances. However, the method by which multimodal data is collected can be significant for recognizing emotional features in language. In this paper, we address the influence data collection methodology has on two multimodal emotion recognition datasets, the IEMOCAP dataset and the OMG-Emotion Behavior dataset, by analyzing textual dataset compositions and emotion recognition accuracy. Experiments with the full IEMOCAP dataset indicate that the composition negatively influences generalization performance when compared to the OMG-Emotion Behavior dataset. We conclude by discussing the impact this may have on HRI experiments.

 

@InProceedings\{SMWW18,
  author       = "Sutherland, Alexander and Magg, Sven and Weber, Cornelius and Wermter, Stefan",
  title        = "Analyzing the Influence of Dataset Composition for Emotion  Recognition",
  booktitle    = "IROS 2018 Workshop on Language and Robotics",
  month        = "Oct",
  year         = "2018",
  url          = "https://www2.informatik.uni-hamburg.de/wtm/publications/2018/SMWW18/SMWW18.pdf"
}

» Alexander Sutherland
» Sven Magg
» Cornelius Weber
» Stefan Wermter