@article{ref1,
title="Estimating fatigue from predetermined speech samples transmitted by operator communication systems",
journal="Proceedings of the ... international driving symposium on human factors in driver assessment, training and vehicle design",
year="2009",
author="Krajewski, Jarek and Trutschel, Udo and Golz, Martin and Sommer, David and Edwards, David",
volume="5",
number="",
pages="468-474",
abstract="We present an estimation of fatigue level within individual operators using voice analysis. One advantage of voice analysis is its utilization of already existing operator communications hardware (2-way radio). From the driver viewpoint it’s an unobtrusive, non-interfering, secondary task. The expected fatigue induced speech changes refer to the voice categories of intensity, rhythm, pause patterns, intonation, speech rate, articulation, and speech quality. Due to inter-individual differences in speech pattern we recorded speaker dependent baselines under alert conditions. Furthermore, sophisticated classification tools (e.g. Support Vector Machine, Multi-Layer Perceptron) were applied to distinguish these different fatigue clusters. To validate the voice analysis predetermined speech samples gained from a driving simulator based sleep deprivation study (N=12; 01.00-08.00 a.m.) are used. Using standard acoustic feature computation procedures we selected 1748 features and fed them into 8 machine learning methods. After each combining the output of each single classifier we yielded a recognition rate of 83.8% in classifying slight from strong fatigue.<p />",
language="",
issn="",
doi="",
url="http://dx.doi.org/"
}