Predicting Fatigue and Psychophysiological Test Performance from Speech for Safety-Critical Environments

Automatic systems for estimating operator fatigue have application in safety-critical environments. A system which could estimate level of fatigue from speech would have application in domains where operators engage in regular verbal communication as part of their duties. Previous studies on the pre...

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Bibliographic Details
Published in:	Frontiers in bioengineering and biotechnology 2015-01, Vol.3, p.124-124
Main Authors:	Baykaner, Khan Richard, Huckvale, Mark, Whiteley, Iya, Andreeva, Svetlana, Ryumin, Oleg
Format:	Article
Language:	eng
Subjects:	Bioengineering and Biotechnology bioinformatics Fatigue machine learning Memory Reaction Time Speech
Online Access:	Get full text
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Summary:	Automatic systems for estimating operator fatigue have application in safety-critical environments. A system which could estimate level of fatigue from speech would have application in domains where operators engage in regular verbal communication as part of their duties. Previous studies on the prediction of fatigue from speech have been limited because of their reliance on subjective ratings and because they lack comparison to other methods for assessing fatigue. In this paper, we present an analysis of voice recordings and psychophysiological test scores collected from seven aerospace personnel during a training task in which they remained awake for 60 h. We show that voice features and test scores are affected by both the total time spent awake and the time position within each subject's circadian cycle. However, we show that time spent awake and time-of-day information are poor predictors of the test results, while voice features can give good predictions of the psychophysiological test scores and sleep latency. Mean absolute errors of prediction are possible within about 17.5% for sleep latency and 5-12% for test scores. We discuss the implications for the use of voice as a means to monitor the effects of fatigue on cognitive performance in practical applications.
ISSN:	2296-4185 2296-4185