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Multi-stream Confidence Analysis for Audio-Visual Affect Recognition.
- Source :
- Affective Computing & Intelligent Interaction; 2005, p964-971, 8p
- Publication Year :
- 2005
-
Abstract
- Changes in a speaker's emotion are a fundamental component in human communication. Some emotions motivate human actions while others add deeper meaning and richness to human interactions. In this paper, we explore the development of a computing algorithm that uses audio and visual sensors to recognize a speaker's affective state. Within the framework of Multi-stream Hidden Markov Model (MHMM), we analyze audio and visual observations to detect 11 cognitive/emotive states. We investigate the use of individual modality confidence measures as a means of estimating weights when combining likelihoods in the audio-visual decision fusion. Person-independent experimental results from 20 subjects in 660 sequences suggest that the use of stream exponents estimated on training data results in classification accuracy improvement of audio-visual affect recognition. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISBNs :
- 9783540296218
- Database :
- Complementary Index
- Journal :
- Affective Computing & Intelligent Interaction
- Publication Type :
- Book
- Accession number :
- 32884303
- Full Text :
- https://doi.org/10.1007/11573548_123