A probabilistic fusion strategy for audiovisual emotion recognition of sparse and noisy data

Jen Chun Lin, Chung Hsien Wu, Wen Li Wei

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

Due to diverse expression styles in real-world scenarios, recognizing human emotions is difficult without collecting sufficient and various data for model training. Besides, emotion recognition of noisy data is another challenging problem to be solved. This work endeavors to propose a fusion strategy to alleviate the problems of noisy and sparse data in bimodal emotion recognition. Toward robust bimodal emotion recognition, a Semi-Coupled Hidden Markov Model (SC-HMM) based on a state-based bimodal alignment strategy is proposed to align the temporal relation of states of two component HMMs between audio and visual streams. Based on this strategy, the SC-HMM can diminish the over-fitting problem and achieve better statistical dependency between states of audio and visual HMMs in sparse data conditions and also provides the ability to better accommodate to the noisy conditions. Experiments show a promising result of the proposed approach.

Original languageEnglish
Title of host publicationICOT 2013 - 1st International Conference on Orange Technologies
Pages278-281
Number of pages4
DOIs
Publication statusPublished - 2013 Jul 12
Event1st International Conference on Orange Technologies, ICOT 2013 - Tainan, Taiwan
Duration: 2013 Mar 122013 Mar 16

Publication series

NameICOT 2013 - 1st International Conference on Orange Technologies

Other

Other1st International Conference on Orange Technologies, ICOT 2013
CountryTaiwan
CityTainan
Period13-03-1213-03-16

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications

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  • Cite this

    Lin, J. C., Wu, C. H., & Wei, W. L. (2013). A probabilistic fusion strategy for audiovisual emotion recognition of sparse and noisy data. In ICOT 2013 - 1st International Conference on Orange Technologies (pp. 278-281). [6521212] (ICOT 2013 - 1st International Conference on Orange Technologies). https://doi.org/10.1109/ICOT.2013.6521212