Automated Speech Recognition for Modeling Team Performance Journal Article uri icon

Overview

abstract

  • While team tasks provide a wealth of data on individual and team performance, techniques for modeling team communication can be quite effortful and time-consuming. Automated techniques of analyzing team discourse provide the promise of quickly judging team performance and permitting feedback to teams both in training and in operations. In previous research, techniques using Latent Semantic Analysis (LSA) have proven successful for analyzing team transcripts. However, converting the audio discourse into transcripts often requires hand transcription. In this work, we describe applying automated speech recognition (ASR) to team transcripts and using the output of the ASR to predict overall team performance. Results indicate that ASR can be used in conjunction with semantic methods of modeling team communication to provide accurate predictions of performance. The work has potential for assisting operators in the performance of their tasks because it can “listen” and in real-time evaluate free-form verbal communication from a variety of sources.

publication date

  • October 1, 2003

Date in CU Experts

  • December 9, 2018 10:47 AM

Full Author List

  • Foltz PW; Laham D; Derr M

author count

  • 3

Other Profiles

International Standard Serial Number (ISSN)

  • 2169-5067

Electronic International Standard Serial Number (EISSN)

  • 1071-1813

Additional Document Info

start page

  • 673

end page

  • 677

volume

  • 47

issue

  • 4