Audio-visual emotion recognition using semi-coupled HMM and error-weighted classifier combination

Jen Chun Lin, Chung-Hsien Wu, Wen Li Wei, Chia Jui Liu

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

Abstract

This paper presents an approach to automatic recognition of emotional states from audio-visual bimodal signals using semi-coupled hidden Markov model and error weighted classifier combination for Human-Computer Interaction (HCI). The proposed model combines a simplified state-based bimodal alignment strategy and a Bayesian classifier weighting scheme to obtain the optimal solution for audio-visual bimodal fusion. The state-based bimodal alignment strategy is proposed to align the temporal relation of the states between audio and visual streams. The Bayesian classifier weighting scheme is adopted to explore the contributions of different audio-visual feature pairs for emotion recognition. For performance evaluation, audio-visual signals with four emotional states (happy, neutral, angry and sad) were collected. Each of the invited four subjects was asked to utter 10 sentences to generate emotional speech and facial expression for each emotion. Experimental results show the efficiency and effectiveness of the proposed method.

Original languageEnglish
Title of host publicationAPSIPA ASC 2010 - Asia-Pacific Signal and Information Processing Association Annual Summit and Conference
Pages903-906
Number of pages4
Publication statusPublished - 2010
Event2nd Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2010 - Biopolis, Singapore
Duration: 2010 Dec 142010 Dec 17

Other

Other2nd Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2010
CountrySingapore
CityBiopolis
Period10-12-1410-12-17

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications
  • Information Systems

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