Augmented Tension Detection in Communication: Insights from Prosodic and Content Features

Bo Zhang, Lu Xiao

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

Abstract

Tension in communication often prevents effective flows of information among members in the conversation and thus can negatively influence practices in teams and learning efficiency at school. Interested in developing a computational technique that automatically detects tension in communication, we explore features that signal the existence of tension in human-human communications and investigate the potential of a supervised learning approach. While there is no tension-annotated dataset available, there are language resources that have distress annotated. Although tension may occur during the communication as a result of various factors, distress creates discomfort and tension. Leveraging an interview dataset that has marked the presence/absence of distress, we investigated the prosodic features and LIWC features that indicate tension. Specifically, we compare 23 prosodic features and LIWC features extracted from 186 interviews in terms of how effective they are to indicate the speaker’s distress in a one-to-one conversation. Our analysis shows that there are seven prosodic features and one LIWC features that differ between distress and non-distress interviews. The seven prosodic features are mean intensity, jitter, shimmer, longest silence duration, longest silence position, standard deviation of interviewee speaking rate, and hesitation. And the one effective LIWC feature is health.

Original languageEnglish (US)
Title of host publicationHuman-Computer Interaction. Multimodal and Natural Interaction - Thematic Area, HCI 2020, Held as Part of the 22nd International Conference, HCII 2020, Proceedings
EditorsMasaaki Kurosu
PublisherSpringer
Pages290-301
Number of pages12
ISBN (Print)9783030490614
DOIs
StatePublished - 2020
EventThematic Area on Human Computer Interaction, HCI 2020, held as part of the 22nd International Conference on Human-Computer Interaction, HCII 2020 - Copenhagen, Denmark
Duration: Jul 19 2020Jul 24 2020

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12182 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

ConferenceThematic Area on Human Computer Interaction, HCI 2020, held as part of the 22nd International Conference on Human-Computer Interaction, HCII 2020
CountryDenmark
CityCopenhagen
Period7/19/207/24/20

Keywords

  • Distress
  • Machine learning
  • Prosodic features
  • Tension

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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  • Cite this

    Zhang, B., & Xiao, L. (2020). Augmented Tension Detection in Communication: Insights from Prosodic and Content Features. In M. Kurosu (Ed.), Human-Computer Interaction. Multimodal and Natural Interaction - Thematic Area, HCI 2020, Held as Part of the 22nd International Conference, HCII 2020, Proceedings (pp. 290-301). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12182 LNCS). Springer. https://doi.org/10.1007/978-3-030-49062-1_20