Predicting Individual Well-Being in Teamwork Contexts Based on Speech Features
Name
information-15-00217.pdf
Size
2.2 MB
Format
Adobe PDF
Checksum (MD5)
aa67d184e6358b3e2dad664a89e67310
Author(s)
Zeulner, Tobias
Hagerer, Gerhard Johann
Müller, Moritz
Vazquez, Ignacio
Gloor, Peter A.
Date Issued
April 12, 2024
Journal
Information
Publisher
MDPI AG
Citation
Zeulner, T.; Hagerer, G.J.; Müller, M.; Vazquez, I.; Gloor, P.A. Predicting Individual Well-Being in Teamwork Contexts Based on Speech Features. Information 2024, 15, 217.
Version
Final published version
Abstract
Current methods for assessing individual well-being in team collaboration at the workplace often rely on manually collected surveys. This limits continuous real-world data collection and proactive measures to improve team member workplace satisfaction. We propose a method to automatically derive social signals related to individual well-being in team collaboration from raw audio and video data collected in teamwork contexts. The goal was to develop computational methods and measurements to facilitate the mirroring of individuals’ well-being to themselves. We focus on how speech behavior is perceived by team members to improve their well-being. Our main contribution is the assembly of an integrated toolchain to perform multi-modal extraction of robust speech features in noisy field settings and to explore which features are predictors of self-reported satisfaction scores. We applied the toolchain to a case study, where we collected videos of 20 teams with 56 participants collaborating over a four-day period in a team project in an educational environment. Our audiovisual speaker diarization extracted individual speech features from a noisy environment. As the dependent variable, team members filled out a daily PERMA (positive emotion, engagement, relationships, meaning, and accomplishment) survey. These well-being scores were predicted using speech features extracted from the videos using machine learning. The results suggest that the proposed toolchain was able to automatically predict individual well-being in teams, leading to better teamwork and happier team members.
MIT Department
System Design and Management Program.
Massachusetts Institute of Technology. Center for Collective Intelligence
Terms of Use
Creative Commons Attribution
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.3390/info15040217
