TY - GEN
T1 - Inferring human interactions in meetings
T2 - 6th International Conference on Ubiquitous Intelligence and Computing, UIC 2009
AU - Yu, Zhiwen
AU - Yu, Zhiyong
AU - Ko, Yusa
AU - Zhou, Xingshe
AU - Nakamura, Yuichi
PY - 2009
Y1 - 2009
N2 - Social dynamics, such as human interaction is important for understanding how a conclusion was reached in a meeting and determining whether the meeting was well organized. In this paper, a multimodal approach is proposed to infer human semantic interactions in meeting discussions. The human interaction, such as proposing an idea, giving comments, expressing a positive opinion, etc., implies user role, attitude, or intention toward a topic. Our approach infers human interactions based on a variety of audiovisual and high-level features, e.g., gestures, attention, speech tone, speaking time, interaction occasion, and information about the previous interaction. Four different inference models including Support Vector Machine (SVM), Bayesian Net, Naïve Bayes, and Decision Tree are selected and compared in human interaction recognition. Our experimental results show that SVM outperforms other inference models, we can successfully infer human interactions with a recognition rate around 80%, and our multimodal approach achieves robust and reliable results by leveraging on the characteristics of each single modality.
AB - Social dynamics, such as human interaction is important for understanding how a conclusion was reached in a meeting and determining whether the meeting was well organized. In this paper, a multimodal approach is proposed to infer human semantic interactions in meeting discussions. The human interaction, such as proposing an idea, giving comments, expressing a positive opinion, etc., implies user role, attitude, or intention toward a topic. Our approach infers human interactions based on a variety of audiovisual and high-level features, e.g., gestures, attention, speech tone, speaking time, interaction occasion, and information about the previous interaction. Four different inference models including Support Vector Machine (SVM), Bayesian Net, Naïve Bayes, and Decision Tree are selected and compared in human interaction recognition. Our experimental results show that SVM outperforms other inference models, we can successfully infer human interactions with a recognition rate around 80%, and our multimodal approach achieves robust and reliable results by leveraging on the characteristics of each single modality.
KW - Human interaction
KW - Multimodal recognition
KW - Smart meeting
UR - http://www.scopus.com/inward/record.url?scp=70350680121&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-02830-4_3
DO - 10.1007/978-3-642-02830-4_3
M3 - 会议稿件
AN - SCOPUS:70350680121
SN - 3642028292
SN - 9783642028298
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 14
EP - 24
BT - Ubiquitous Intelligence and Computing - 6th International Conference, UIC 2009, Proceedings
Y2 - 7 July 2009 through 9 July 2009
ER -