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Towards an Engagement-Aware Attentive Artificial Listener for Multi-Party Interactions.

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  • 1Department of Intelligent Systems, Interactive Intelligence, Delft University of Technology, Delft, Netherlands.

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Understanding attentive listening in group conversations helps robots interact better. This study analyzes human nonverbal cues, like gaze patterns, to develop robots that appear more engaged and improve human-robot communication in multi-party settings.

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artificial listenereye-gaze patternshead gestureshuman-robot interactionmulti-party interactionsnon-verbal behaviorssocial signal processing

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Area of Science:

  • Human-computer interaction
  • Social robotics
  • Communication studies

Background:

  • Attentive listening is crucial for human interaction, involving both information gathering and signaling engagement through nonverbal cues.
  • Nonverbal cue signaling becomes more complex in multi-party conversations compared to dyadic interactions.
  • Understanding human listening behavior is vital for designing effective human-robot interactions.

Purpose of the Study:

  • To analyze listener gaze patterns and feedback behavior in human-human multi-party interactions.
  • To investigate if human-like listening cues enhance robot perception in human-robot interactions.
  • To develop and evaluate an attentive listening system for robots.

Main Methods:

  • Analysis of nonverbal listener behaviors in human-human multi-party dialogues.
  • Implementation of an attentive listening system generating multi-modal robot behavior.
  • Comparative evaluation of the attentive system against a baseline system.

Main Results:

  • The study investigates the effectiveness of human-derived gaze patterns for robot behavior generation.
  • An attentive listening system is developed and compared to a baseline system.
  • Evaluation focuses on participant and observer perceptions of the robot's behavior.

Conclusions:

  • Findings will inform the design of robots capable of more natural and engaging multi-party interactions.
  • Successful implementation of attentive listening behaviors can improve user experience in human-robot collaboration.
  • This research bridges the gap between human communication dynamics and robotic social intelligence.