Recognition of stress in speech using wavelet analysis and Teager energy operator

The automatic recognition and classification of speech under stress has applications in behavioural and mental health sciences, human to machine communication and robotics. The majority of recent studies are based on a linear model of the speech signal. In this study, the nonlinear Teager Energy Operator (TEO) analysis was used to derive the classification features. Moreover, the TEO analysis was combined with the Discrete Wavelet Transform, Wavelet Packet and Perceptual Wavelet Packet transforms to produce the Normalised TEO Autocorrelation Envelope Area coefficients for the classification process. The classification was performed using a Gaussian Mixture Model under speaker-independent conditions. The speech was classified into two classes: neutral and stressed. The best overall performance was observed for the features extracted using TEO analysis in combination with the Perceptual Wavelet Packet method. The accuracy in this case ranges from 94% to 96% depending on the type of mother wavelet.