Stress Identification from Speech Signal. Identification of Stress from Speech Signal Using Soft Computing Techniques

      Аннотация: The definitions of speech signal and speech under stress covering stress and emotions including its application in the areas of stress identification. The motivation of the research on stressed speech is explained with problem definition. An overview of stressed and emotion speech databases with the existing feature extraction methods and different neural networks classifiers techniques. The need of the research in stressed speech is explained. Objectives of the research work are given. In the research work I describe the contents of the speech databases used in this study including the number of samples for each databases, also the feature extraction method mel frequency (MFCC) and different neural classifiers (MLP, SVM, FFBPNN, RNN, ANFIS and RBF) including twelve various training rules used for an automatic stress identification in speech. Further investigates two different algorithms for stress detection which are adaptive neuro fuzzy inference system and adaptive empirical mode decomposition with its implementation in Matlab software R2014.

Ключевые слова: MFCC, SVM, ANFIS, RNN, FFBPNN