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Due to the advance of speech technologies and their increasing usage in various applications, automatic recognition of emotions in speech represents one of the emerging fields in human-computer interaction. This paper deals with several topics related to automatic emotional speech recognition, most notably with the improvement of recognition accuracy by lowering the dimensionality of the feature space and evaluation of the relevance of particular feature types. The research is focused on the classification of emotional speech into five basic emotional classes (anger, joy, fear, sadness and neutral speech) using a recorded corpus of emotional speech in Serbian.

Relevance of the types and the statistical properties of features in the recognition of basic emotions in speech