The Prediction of Fatigue Using Speech as a Biosignal
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.
KeywordsFatigue Speech Computational paralinguistics
The authors would like to acknowledge the European Space Agency and University College of London who are jointly responsible for funding this work.
- 1.Dobbie, K.: Fatigue-related crashes: an analysis of fatigue-related crashes on Australian roads using an operational definition of fatigue. Australian transport safety bureau (OR23), (2002)Google Scholar
- 2.FMCSA: Regulatory impact analysis – hours of service final rule. Federal motor carrier safety administration, December 2011Google Scholar
- 5.Barr, L., Howarth, H., Popkin, S., Carroll, R.: A review and evaluation of emerging driver fatigue detection measures and technologies. John A. Volpe National Transportation Systems Center (2005)Google Scholar
- 6.Begum, S.: Intelligent driver monitoring systems based on physiological sensor signals: a review. In: IEEE Annual Conference on Intelligent Transportation Systems (ITSC) (2013)Google Scholar
- 8.Schuller, B., Batliner, A., Steidl, S., Schiel, F., Zrajewski, F.: The Interspeech 2011 Speaker state challenge. In: Proceedings of Interspeech 2011, pp. 2301–2304 (2011)Google Scholar
- 9.Huang, D., Ge, S., Zhang, Z.: Speaker State Classification Based on Fusion of Asymmetric SIMPLS and Support Vector Machines. In: Proceedings of the Interspeech 2011, pp. 3301–3304 (2011)Google Scholar
- 15.Hsu, C., Chang, C., Lin, C.: A practical guide to support vector classification. Department of computer science technical report, National Taiwan University (2010)Google Scholar
- 17.Chen, S., Gopinath, R.: Gaussianization. In: Proceedings of the NIPS 2000, Denver Colorado (2000)Google Scholar