Automated Speech Recognition for Modeling Team Performance

Autor:	Peter W. Foltz, Darrell Laham, Marcia A. Derr
Rok vydání:	2003
Předmět:	Medical Terminology Engineering business.industry Latent semantic analysis Team communication Speech recognition 05 social sciences 0501 psychology and cognitive sciences Transcription (software) business 050107 human factors 050105 experimental psychology Medical Assisting and Transcription
Zdroj:	Proceedings of the Human Factors and Ergonomics Society Annual Meeting. 47:673-677
ISSN:	1071-1813 2169-5067
DOI:	10.1177/154193120304700402
Popis:	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.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::28157888850db316de16a9fbab2f7039 https://doi.org/10.1177/154193120304700402 Zobrazit plný text záznamu