Predicting Group Satisfaction in Meeting Discussions

Catherine Lai, Gabriel Murray

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract / Description of output

We address the task of automatically predicting group satisfaction in meetings using acoustic, lexical, and turn-taking features. Participant satisfaction is measured using post-meeting ratings from the AMI corpus. We focus on predicting three aspects of satisfaction: overall satisfaction, participant attention satisfaction, and information overload. All predictions are made at the aggregated group level. In general, we find that combining features across modalities improves prediction performance. However, feature ablation significantly improves performance. Our experiments also show how data-driven methods can be used to explore how different facets of group satisfaction are expressed through different modalities. For example, inclusion of prosodic features improves prediction of attention satisfaction but hinders prediction of overall satisfaction, but the opposite for lexical features. Moreover, feelings of sufficient attention were better reflected by acoustic features than by speaking time, while information overload was better reflected by specific lexical cues and turn-taking patterns. Overall, this study indicates that group affect can be revealed as much by how participants speak, as by what they say.
Original languageEnglish
Title of host publicationWorkshop on Modeling Cognitive Processes from Multimodal Data (MCPMD'18)
Number of pages8
ISBN (Electronic)978-1-4503-6072-2
Publication statusPublished - 16 Oct 2018
EventWorkshop on Modeling Cognitive Processes from Multimodal Data 2018 - Boulder, United States
Duration: 16 Oct 201816 Oct 2018


ConferenceWorkshop on Modeling Cognitive Processes from Multimodal Data 2018
Abbreviated titleICMI 2018
Country/TerritoryUnited States
Internet address

Keywords / Materials (for Non-textual outputs)

  • multimodal dialogue
  • Affective computing
  • speech and language processing
  • sentiment


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