The traditional way of emotional classification involves using the two-dimensional (2D) emotional model by Thayer, which identifies emotion by arousal and valence. The 2D model is not fine enough to classify among the rich vocabularies of emotions, such as distinguish between disgusting and fear. Another problem of the traditional methods is that they don’t have a formal definition of the axis value of the emotional model. They either assign the axis value manually or rate them by listening test. We propose to use the PAD (Pleasure, Arousal, Dominance) emotional state model to describe speech emotion in a continuous 3-dimensional scale. We suggest an initial definition of the continuous axis values by observing into the pattern of Log Frequency Power Coefficients (LFPC) fluctuation. We verify the result using a database of German emotional speech. Experiments show that the classification result of a set of big-6 emotions on average is 81%.

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