Analyzing the Influence of Dataset Composition for Emotion Recognition

• URL: http://arxiv.org/abs/2103.03700v1
• Date: Fri, 5 Mar 2021 14:20:59 GMT
• Title: Analyzing the Influence of Dataset Composition for Emotion Recognition
• Authors: A. Sutherland, S. Magg, C. Weber, S. Wermter
• Abstract summary: We analyze the influence data collection methodology has on two multimodal emotion recognition datasets. Experiments with the full IEMOCAP dataset indicate that the composition negatively influences generalization performance when compared to the OMG-Emotion Behavior dataset.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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.

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