Conference Proceeding

Estimating Fatigue from Predetermined Speech Samples Transmitted by Operator Communication Systems

Authors: , , , ,

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.

Keywords:

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). doi: https://doi.org/10.17077/drivingassessment.1359