Abstract
Automatic systems for estimating operator fatigue have application in safety-critical environments. We develop and evaluate a system to detect fatigue from speech recordings collected from speakers kept awake over a 60-hour period. A binary classification system (fatigued/not-fatigued) based on time spent awake showed good discrimination, with 80 % unweighted accuracy using raw features, and 90 % with speaker-normalized features. We describe the data collection, feature analysis, machine learning and cross-validation used in the study. Results are promising for real-world applications in domains such as aerospace, transportation and mining where operators are in regular verbal communication as part of their normal working activities.
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The authors would like to acknowledge the European Space Agency and University College of London who are jointly responsible for funding this work.
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Baykaner, K., Huckvale, M., Whiteley, I., Ryumin, O., Andreeva, S. (2015). The Prediction of Fatigue Using Speech as a Biosignal. In: Dediu, AH., Martín-Vide, C., Vicsi, K. (eds) Statistical Language and Speech Processing. SLSP 2015. Lecture Notes in Computer Science(), vol 9449. Springer, Cham. https://doi.org/10.1007/978-3-319-25789-1_2
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DOI: https://doi.org/10.1007/978-3-319-25789-1_2
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