CASIA OpenIR  > 模式识别国家重点实验室  > 语音交互
Bayesian Inference based Temporal Modeling for Naturalistic Affective Expression Classification
Linlin Chao; Jianhua Tao; Minghao Yang
2013
Conference NameHumaine Association Conference on Affective Computing and Intelligent Interaction, ACII 2013
Source Publication2013 Humaine Association Conference on Affective Computing and Intelligent Interaction
Pages173-178
Conference Date2013-9
Conference PlaceGeneva, Switzerland
Abstract1; in real life, the affective state of human beings changes gradually and smoothly. There is a high probability that the affective state of a certain moment depends on the states of a previous period. In this study, we propose to explicitly model the temporal relationship using a Bayesian inference based two-stage classification approach. This approach could involve knowledge about the dynamics of affective states during a period, so that the inferred affective states are predicted by considering a certain amount of context. Evaluations on the Audio Sub-Challenge of the 2011 Audio/Visual Emotion Challenge show our approach obtains competitive results to those of Audio Sub-Challenge winners. The temporal context modeling method proposed in this paper is also helpful for other sequential pattern recognition problems.
KeywordAffect Dimensions
Indexed ByEI
Language英语
Document Type会议论文
Identifierhttp://ir.ia.ac.cn/handle/173211/11844
Collection模式识别国家重点实验室_语音交互
Corresponding AuthorLinlin Chao
AffiliationInstitute of Automation, Chinese Academy of Sciences
Recommended Citation
GB/T 7714
Linlin Chao,Jianhua Tao,Minghao Yang. Bayesian Inference based Temporal Modeling for Naturalistic Affective Expression Classification[C],2013:173-178.
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