Features extraction and selection for emotional speech classification

The classification of emotional speech is a topic in speech recognition with more and more interest, and it has giant prospect in applications in a wide variety of fields. It is an important preparation for automatic classification and recognition of emotions to select a proper feature set as a description to the emotional speech, and to find a proper definition to the emotions in speech. The speech samples used in this paper come from Berlin database which contains 7 kinds of emotions, with 207 speech samples of male voice and 287 speech samples of female voice. A feature set of 50 potentially features is extracted and analyzed, and the best features are selected. A definition of emotions as 3-states emotions is also proposed in this paper.