Methods for stress classification: nonlinear TEO and linear speech based features

Speech production variations due to perceptually induced stress contribute significantly to reduced speech processing performance. One approach that can improve the robustness of speech processing (e.g., recognition) algorithms against stress is to formulate an objective classification of speaker stress based upon the acoustic speech signal. An overview of methods for stress classification is presented. First, we review traditional pitch-based methods for stress detection and classification. Second, neural network based stress classifiers with cepstral-based features, as well as wavelet-based classification algorithms are considered. The effect of stress on linear speech features is discussed, followed by the application of linear features and the Teager (1990) energy operator (TEO) based nonlinear features for effective stress classification. A new evaluation for stress classification and assessment is presented using a critical band frequency partition based the TEO feature and the combination of several linear features. Results using NATO databases of actual speech under stress are presented. Finally, we discuss issues relating to stress classification across known and unknown speakers and suggest areas for further research