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Exploring sources of variation in human behavioral data: Towards automatic audio-visual emotion recognition

My PhD work aims at developing computational methodologies for automatic emotion recognition from audiovisual behavioral data. A main challenge in automatic emotion recognition is that human behavioral data are highly complex, due to multiple sources that vary and modulate behaviors. My goal is to p...

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Main Author:	Kim, Yelin
Format:	Conference Proceeding
Language:	English
Subjects:	affective computing Analytical models Audiovisual Automation Communities Computation Conferences emotion estimation Emotion recognition Emotions Human behavior human perception Motion segmentation multimodal Production Recognition Speech Speech recognition temporal variation Visualization
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creator	Kim, Yelin
description	My PhD work aims at developing computational methodologies for automatic emotion recognition from audiovisual behavioral data. A main challenge in automatic emotion recognition is that human behavioral data are highly complex, due to multiple sources that vary and modulate behaviors. My goal is to provide computational frameworks for understanding and controlling for multiple sources of variation in human behavioral data that co-occur with the production of emotion, with the aim of improving automatic emotion recognition systems [1]-[6]. In particular, my research aims at providing representation, modeling, and analysis methods for complex and time-changing behaviors in human audio-visual data by introducing temporal segmentation and time-series analysis techniques. This research contributes to the affective computing community by improving the performance of automatic emotion recognition systems and increasing the understanding of affective cues embedded within complex audio-visual data.
doi_str_mv	10.1109/ACII.2015.7344653
format	conference_proceeding
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subjects	affective computing Analytical models Audiovisual Automation Communities Computation Conferences emotion estimation Emotion recognition Emotions Human behavior human perception Motion segmentation multimodal Production Recognition Speech Speech recognition temporal variation Visualization
title	Exploring sources of variation in human behavioral data: Towards automatic audio-visual emotion recognition
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