Study of empirical mode decomposition and spectral analysis for stress and emotion classification in natural speech

Abstract

Two new approaches to the feature extraction process for automatic stress and emotion classification in speech are proposed and examined. The first method uses the empirical model decomposition (EMD) of speech into intrinsic mode functions (IMF) and calculates the average Renyi entropy for the IMF channels. The second method calculates the average spectral energy in the sub-bands of speech spectrograms and can be enhanced by anisotropic diffusion filtering of spectrograms. In the second method, three types of sub-bands were examined: critical, Bark and ERB. The performance of the new features was compared with the conventional mel frequency cepstral coefficients (MFCC) method. The modeling a..