Applying nonlinear dynamics features for speech-based fatigue detection

Authors:
Jarek Krajewski;David Sommer;Thomas Schnupp;Tom Laufenberg;Christian Heinze;Martin Golz
Affiliations:
University Wuppertal, Wuppertal, Germany;University of Applied Sciences Schmalkalden, Schmalkalden, Germany;University of Applied Sciences Schmalkalden, Schmalkalden, Germany;University Wuppertal, Wuppertal, Germany;University of Applied Sciences Schmalkalden, Schmalkalden, Germany;University of Applied Sciences Schmalkalden, Schmalkalden, Germany
Venue:
Proceedings of the 7th International Conference on Methods and Techniques in Behavioral Research
Year:
2010

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Abstract

This paper describes a speech signal processing method to measure fatigue from speech. The advantages of this realtime approach are that obtaining speech data is non obtrusive, free from sensor application and calibration efforts. Applying methods of Non Linear Dynamics(NLD) provides additional information regarding the dynamics and structure of fatigue speech comparing to the commonly applied speech emotion recognition feature set (e.g. fundamental frequency, intensity, pause patterns, formants, cepstral coefficients). We achieved significant correlations between fatigue and NLD features of 0.29. The validity of this approach is briefly discussed by summarizing the empirical results of a sleep deprivation study.