Stress Detection Using Speech Spectrograms and Sigma-pi Neuron Units

This paper presents a new system for automatic stress detection in speech. In the process of feature extraction speech spectrograms were used as the primary features. The sigma-pi neuron cells were then employed to derive the secondary features. The analysis was performed at three alternative sets of analytical frequency bands: critical bands, Bark scale bands and equivalent rectangular bandwidth (ERB) scale bands. The presented algorithm was tested using actual stressful speech utterances from SUSAS (Speech Under Simulated and Actual Stress) database on the vowel-based level. The automatic stress-level classification was implemented using Gaussian mixture model (GMM) and k-nearest neighbor (KNN) classifiers. The strongest effect on the classification results was observed when selecting the type of frequency bands. The ERB scale provided the highest classification results ranging from 67.84% to 73.76%. The classification results did not differ between data sets containing specific types of vowels and data sets containing mixtures of vowels. This indicates that the proposed method can be applied to voiced speech in speech independent conditions.