Conference Proceeding

Estimating Fatigue from Predetermined Speech Samples Transmitted by Operator Communication Systems

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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.

How to Cite:

Krajewski, J. & Trutschel, U. & Golz, M. & Sommer, D. & Edwards, D., (2009) “Estimating Fatigue from Predetermined Speech Samples Transmitted by Operator Communication Systems”, Driving Assessment Conference 5(2009), 468-474. doi: https://doi.org/10.17077/drivingassessment.1359

Rights: Copyright © 2009 the author(s)