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Multimedia Tools and Applications

, Volume 74, Issue 22, pp 10025–10051 | Cite as

User behavior fusion in dialog management with multi-modal history cues

  • Minghao YangEmail author
  • Jianhua Tao
  • Linlin Chao
  • Hao Li
  • Dawei Zhang
  • Hao Che
  • Tingli Gao
  • Bin Liu
Article

Abstract

It enhances user experience by making the talking avatar be sensitive to user behaviors in human computer interaction (HCI). In this study, we combine user’s multi-modal behaviors with behaviors’ historical information in dialog management (DM) to improve the avatar’s sensitivity not only to user explicit behavior (speech command) but also to user supporting expression (emotion and gesture, etc.). In the dialog management, according to the different contributions of facial expression, gesture and head motion to speech comprehension, we divide the user’s multi-modal behaviors into three categories: complementation, conflict and independence. The behavior categories could be first automatically obtained from a short-term and time-dynamic (STTD) fusion model with audio-visual input. Different behavior category leads to different avatar’s response in later dialog turns. Usually, the conflict behavior reflects user’s ambiguous intention (for example: user says “no” while he (her) is smiling). In this case, the trial-and-error schema is adopted to eliminate the conversation ambiguity. For the later dialog process, we divide all the avatar dialog states into four types: “Ask”, “Answer”, “Chat” and “Forget”. With the detection of complementation and independence behaviors, the user supporting expression as well as his (her) explicit behavior could be estimated as triggers for topic maintenance or transfer among four dialog states. At the first section of experiments, we discuss the reliability of STTD model for user behavior classification. Based on the proposed dialog management and STTD model, we continue to construct a drive route information query system by connecting the user behavior sensitive dialog management (BSDM) to a 3D talking avatar. The practical conversation records of avatar with different users show that the BSDM makes the avatar be able to understand and be sensitive to the users’ facial expressions, emotional voice and gesture, which improves user experience on multi-modal human computer conversation.

Keywords

Dialog management (DM) Multi-modal data fusion Human computer interaction (HCI) Emotion detection 

Notes

Acknowledgments

This work is supported by the National Natural Science Foundation of China (NSFC) (No. 61273288, No. 61233009, No. 61203258, No. 61530503, No. 61332017, No. 61375027), and the Major Program for the National Social Science Fund of China (13&ZD189).

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Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  • Minghao Yang
    • 1
    Email author
  • Jianhua Tao
    • 1
  • Linlin Chao
    • 1
  • Hao Li
    • 1
  • Dawei Zhang
    • 1
  • Hao Che
    • 1
  • Tingli Gao
    • 1
  • Bin Liu
    • 1
  1. 1.The National Laboratory of Pattern Recognition, Institute of AutomationChinese Academy of SciencesBeijingChina

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