The paper describes two experimental studies on vocal emotion expression and recognition. The first study deals with a corpus of 700 short utterances expressing five emotions: happiness, anger, sadness, fear, and normal (unemotional) state, which were portrayed by thirty non-professional actors. After evaluation, a part of this corpus was used for extracting features and training back propagation neural network models. Some statistics of the pitch, the first and second formants, energy and the speaking rate were selected as relevant features using feature selection techniques. Several neural network recognizers and ensembles of recognizers were created.
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