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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Bibliographic Details
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
Citations: Items that cite this one
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Description
Summary: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.
ISSN:2156-8111
DOI:10.1109/ACII.2015.7344653