Analysis and Detection of Speech under Emotional Stress

Automatic detection of emotional stress in speech has recently drawn increasing attention, mainly in the fields of natural and spoken language processing. Stress can be automatically detected from speech-derived parameters, such as fundamental frequency F0, voice intensity, jitter, shimmer, harmonic-to-noise ratio, mel-frequency cepstral coefficients, etc. In this paper, we focus on the analysis of selected voice characteristics in available datasets of emotional speech and speech under stress in the English and Slovak languages. We also proposed a stress level detection approach, which uses the fusion of the Teager energy operator (TEO) with mel-frequency cepstral coefficients (MFCCs) and classification based on decision trees and ensemble learning method of bagging decision trees. Preliminary results of 64% classification accuracy are comparable to other studies in this field of research.