The following paper introduces a set of novel descriptors of emotional speech, which allows for a significant increase in emotion classification performance. The proposed characteristics - statistical properties of Poincare Maps, derived for voiced-speech segments of utterances - are used in recognition in combinations with a variety of both commonly used and some other, original descriptors of emotional speech. The introduced features proved to provide useful information into a classification process. Emotion recognition is performed using binary decision trees, which perform extraction of different emotions at consecutive decision levels. Classification rates for the considered six-category problem, which involved anger, boredom, joy, fear, neutral and sadness, are at the level up to 79% for both speaker-dependent and speaker-independent cases.
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