In this paper, a system for speech emotion analysis is presented. On a corpus of over 1700 utterances from an individual, the feature vector stream is extracted for each utterance based on short time log frequency power coefficients (LFCC). Using the feature vector streams, we trained Hidden Markov Models (HMMs) to recognize seven basic categories emotions: neutral, happiness, anger, sadness, surprise, fear. Furthermore, the intensity of the basic emotion is divided into 3 levels. And we trained 18 sub-HMMs to identify the intensity of the recognized emotions. Experiment result shows that the emotion recognition rate and the estimation of intensity performed by our system are of good and convincing quality.
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