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  1. Proceedings of the Workshop on Multimodal Analyses enabling Artificial Agents in Human-Machine Interaction (MA3HMI '16)
  2. Annotation and analysis of listener's engagement based on multi-modal behaviors
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Annotation and analysis of listener's engagement based on multi-modal behaviors

Content Provider ACM Digital Library
Author Lala, Divesh Takanashi, Katsuya Kawahara, Tatsuya Inoue, Koji Nakamura, Shizuka
Abstract We address the annotation of engagement in the context of human-machine interaction. Engagement represents the level of how much a user is being interested in and willing to continue the current interaction. The conversational data used in the annotation work is a human-robot interaction corpus where a human subject talks with the android ERICA, which is remotely operated by another human subject. The annotation work was done by multiple third-party annotators, and the task was to detect the time point when the level of engagement becomes high. The annotation results indicate that there are agreements among the annotators although the numbers of annotated points are different among them. It is also found that the level of engagement is related to turn-taking behaviors. Furthermore, we conducted interviews with the annotators to reveal behaviors used to show a high level of engagement. The results suggest that laughing, backchannels and nodding are related to the level of engagement.
Starting Page 25
Ending Page 32
Page Count 8
File Format PDF
ISBN 9781450345620
DOI 10.1145/3011263.3011271
Language English
Publisher Association for Computing Machinery (ACM)
Publisher Date 2016-11-12
Publisher Place New York
Access Restriction Subscribed
Subject Keyword Engagement Multi-modal Human-machine interaction Behavior Annotation Turn taking
Content Type Text
Resource Type Article
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