tailieunhanh - Báo cáo hóa học: " Utterance independent bimodal emotion recognition in spontaneous communication"

Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành hóa học dành cho các bạn yêu hóa học tham khảo đề tài: Utterance independent bimodal emotion recognition in spontaneous communication | Tao et al. EURASIP Journal on Advances in Signal Processing 2011 2011 4 http content 2011 1 4 o EURASIP Journal on Advances in Signal Processing a SpringerOpen Journal RESEARCH Open Access Utterance independent bimodal emotion recognition in spontaneous communication Jianhua Tao Shifeng Pan Minghao Yang Ya Li Kaihui Mu and Jianfeng Che Abstract Emotion expressions sometimes are mixed with the utterance expression in spontaneous face-to-face communication which makes difficulties for emotion recognition. This article introduces the methods of reducing the utterance influences in visual parameters for the audio-visual-based emotion recognition. The audio and visual channels are first combined under a Multistream Hidden Markov Model MHMM . Then the utterance reduction is finished by finding the residual between the real visual parameters and the outputs of the utterance related visual parameters. This article introduces the Fused Hidden Markov Model Inversion method which is trained in the neutral expressed audio-visual corpus to solve the problem. To reduce the computing complexity the inversion model is further simplified to a Gaussian Mixture Model GMM mapping. Compared with traditional bimodal emotion recognition methods . SVM CART Boosting the utterance reduction method can give better results of emotion recognition. The experiments also show the effectiveness of our emotion recognition system when it was used in a live environment. Keywords Bimodal emotion recognition Utterance Independent Multistream Hidden Markov Model Fused Hidden Markov Model Inversion Introduction The last two decades have seen significant effort devoted to developing methods for automatic human emotion recognition . 1-15 which is an attractive research issue due to its great potential in human-computer interactions HCIs virtual reality etc. Although there are a few tentative efforts to detect non-basic emotion states including fatigue . 16 and mental states

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