Leimin Tian

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In this paper, we investigate the use of high-level features for recognizing human emotions at the word-level in natural conversations with virtual agents. Experiments were carried out on the 2012 Au-dio/Visual Emotion Challenge (AVEC2012) database, where emotions are defined as vectors in the Arousal-Expectancy-Power-Valence emotional space. Our model(More)
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Current research in emotion recognition focuses on identifying better feature representations and recognition models. The goal of this project is to improve on current automatic emotion recognition performance by identifying more predic-tive knowledge-driven features, and by building a hierarchical contextual model that combines state-of-the-art statistical(More)
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