12 May 2016 Self-organizing map classifier for stressed speech recognition
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This paper presents a method for detecting speech under stress using Self-Organizing Maps. Most people who are exposed to stressful situations can not adequately respond to stimuli. Army, police, and fire department occupy the largest part of the environment that are typical of an increased number of stressful situations. The role of men in action is controlled by the control center. Control commands should be adapted to the psychological state of a man in action. It is known that the psychological changes of the human body are also reflected physiologically, which consequently means the stress effected speech. Therefore, it is clear that the speech stress recognizing system is required in the security forces. One of the possible classifiers, which are popular for its flexibility, is a self-organizing map. It is one type of the artificial neural networks. Flexibility means independence classifier on the character of the input data. This feature is suitable for speech processing. Human Stress can be seen as a kind of emotional state. Mel-frequency cepstral coefficients, LPC coefficients, and prosody features were selected for input data. These coefficients were selected for their sensitivity to emotional changes. The calculation of the parameters was performed on speech recordings, which can be divided into two classes, namely the stress state recordings and normal state recordings. The benefit of the experiment is a method using SOM classifier for stress speech detection. Results showed the advantage of this method, which is input data flexibility.
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Pavol Partila, Pavol Partila, Jaromir Tovarek, Jaromir Tovarek, Miroslav Voznak, Miroslav Voznak, "Self-organizing map classifier for stressed speech recognition", Proc. SPIE 9850, Machine Intelligence and Bio-inspired Computation: Theory and Applications X, 98500A (12 May 2016); doi: 10.1117/12.2224253; https://doi.org/10.1117/12.2224253

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