Applying nonlinear dynamics features for speech-based fatigue detection

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.