Audiovisual Behavior Modeling by Combined Feature Spaces

Björn Wolfgang Schuller · Dejan Arsić · Gerhard Rigoll · Bernd Radig

82 citations0/0 files storedIEEE International Conference on Acoustics Speech and Signal Processing

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Emotion and Mood Recognition Speech and Audio Processing Human Pose and Action Recognition

Abstract

Great interest is recently shown in behavior modeling, especially in public surveillance tasks. In general it is agreed upon the benefits of use of several input cues as audio and video. Yet, synchronization and fusion of these information sources remains the main challenge. We therefore show results for a feature space combination, which allows for overall feature space optimization. Audio and video features are thereby firstly derived as low-level-descriptors. Synchronization and feature combination is achieved by multivariate time-series analysis. Test-runs on a database of aggressive, cheerful, intoxicated, nervous, neutral, and tired behavior in an airplane situation show a significant improvement over each single modality.

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References

2001Emotion recognition in human-computer interaction 2003Facial expression recognition from video sequences: temporal and static modeling 2003Toward an affect-sensitive multimodal human-computer interaction 2003Hidden Markov model-based speech emotion recognition 2003Hidden Markov model-based speech emotion recognition 2005Speaker independent emotion recognition by early fusion of acoustic and linguistic features within ensembles 2006Timing levels in segment-based speech emotion recognition

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