Audio-visual emotion recognition with multilayer boosted HMM  

作　　者：吕坤 贾云得 张欣 

机构地区：[1]School of Software,Beijing Institute of Technology [2]Beijing Lab of Intelligent Information Technology,School of Computer Science,Beijing Institute of Technology

出　　处：《Journal of Beijing Institute of Technology》2013年第1期89-93,共5页北京理工大学学报（英文版）

基　　金：Supported by the National Natural Science Foundation of China(60905006);the NSFC-Guangdong Joint Fund(U1035004)

摘　　要：Emotion recognition has become an important task of modern human-computer interac- tion. A multilayer boosted HMM （ MBHMM ） classifier for automatic audio-visual emotion recognition is presented in this paper. A modified Baum-Welch algorithm is proposed for component HMM learn- ing and adaptive boosting （AdaBoost） is used to train ensemble classifiers for different layers （cues）. Except for the first layer, the initial weights of training samples in current layer are decided by recognition results of the ensemble classifier in the upper layer. Thus the training procedure using current cue can focus more on the difficult samples according to the previous cue. Our MBHMM clas- sifier is combined by these ensemble classifiers and takes advantage of the complementary informa- tion from multiple cues and modalities. Experimental results on audio-visual emotion data collected in Wizard of Oz scenarios and labeled under two types of emotion category sets demonstrate that our approach is effective and promising.Emotion recognition has become an important task of modern human-computer interac- tion. A multilayer boosted HMM （ MBHMM ） classifier for automatic audio-visual emotion recognition is presented in this paper. A modified Baum-Welch algorithm is proposed for component HMM learn- ing and adaptive boosting （AdaBoost） is used to train ensemble classifiers for different layers （cues）. Except for the first layer, the initial weights of training samples in current layer are decided by recognition results of the ensemble classifier in the upper layer. Thus the training procedure using current cue can focus more on the difficult samples according to the previous cue. Our MBHMM clas- sifier is combined by these ensemble classifiers and takes advantage of the complementary informa- tion from multiple cues and modalities. Experimental results on audio-visual emotion data collected in Wizard of Oz scenarios and labeled under two types of emotion category sets demonstrate that our approach is effective and promising.

关 键 词：emotion recognition audio-visual fusion Baum-Welch algorithm multilayer boostedHMM Wizard of Oz scenario 

分 类 号：TP391.4[自动化与计算机技术—计算机应用技术]