Error weighted semi-coupled hidden markov model for audio-visual emotion recognition

Jen Chun Lin, Chung Hsien Wu, Wen Li Wei

研究成果: Article同行評審

105 引文 斯高帕斯(Scopus)

摘要

This paper presents an approach to the automatic recognition of human emotions from audio-visual bimodal signals using an error weighted semi-coupled hidden Markov model (EWSC-HMM). The proposed approach combines an SC-HMM with a state-based bimodal alignment strategy and a Bayesian classifier weighting scheme to obtain the optimal emotion recognition result based on audio-visual bimodal fusion. The state-based bimodal alignment strategy in SC-HMM is proposed to align the temporal relation between audio and visual streams. The Bayesian classifier weighting scheme is then adopted to explore the contributions of the SC-HMM-based classifiers for different audio-visual feature pairs in order to obtain the emotion recognition output. For performance evaluation, two databases are considered: the MHMC posed database and the SEMAINE naturalistic database. Experimental results show that the proposed approach not only outperforms other fusion-based bimodal emotion recognition methods for posed expressions but also provides satisfactory results for naturalistic expressions.

原文English
文章編號6042338
頁(從 - 到)142-156
頁數15
期刊IEEE Transactions on Multimedia
14
發行號1
DOIs
出版狀態Published - 2012 2月

All Science Journal Classification (ASJC) codes

  • 訊號處理
  • 媒體技術
  • 電腦科學應用
  • 電氣與電子工程

指紋

深入研究「Error weighted semi-coupled hidden markov model for audio-visual emotion recognition」主題。共同形成了獨特的指紋。

引用此