Semi-coupled hidden Markov model with state-based alignment strategy for audio-visual emotion recognition

Jen Chun Lin, Chung-Hsien Wu, Wen Li Wei

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

2 Citations (Scopus)

Abstract

This paper presents an approach to bi-modal emotion recognition based on a semi-coupled hidden Markov model (SC-HMM). A simplified state-based bi-modal alignment strategy in SC-HMM is proposed to align the temporal relation of states between audio and visual streams. Based on this strategy, the proposed SC-HMM can alleviate the problem of data sparseness and achieve better statistical dependency between states of audio and visual HMMs in most real world scenarios. For performance evaluation, audio-visual signals with four emotional states (happy, neutral, angry and sad) were collected. Each of the invited seven subjects was asked to utter 30 types of sentences twice to generate emotional speech and facial expression for each emotion. Experimental results show the proposed bi-modal approach outperforms other fusion-based bi-modal emotion recognition methods.

Original languageEnglish
Title of host publicationAffective Computing and Intelligent Interaction - 4th International Conference, ACII 2011, Proceedings
Pages185-194
Number of pages10
EditionPART 1
DOIs
Publication statusPublished - 2011 Oct 27
Event4th International Conference on Affective Computing and Intelligent Interaction, ACII 2011 - Memphis, TN, United States
Duration: 2011 Oct 92011 Oct 12

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 1
Volume6974 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other4th International Conference on Affective Computing and Intelligent Interaction, ACII 2011
CountryUnited States
CityMemphis, TN
Period11-10-0911-10-12

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

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