This paper examiens three algorithms to recognize speaker's emotion using the speech signals. Target emotions are happiness, sadness, anger, fear, boredom and neutral state. MLB(Maximum-Likelihood Bayes), NN(Nearest Neighbor) and HMM(Hidden Markov Model) algorithms are used as the pattern matcing techniques. In all cases, pitch and energy are used as the features. The feature vectors for MLB and NN are composed of pitch mena, pitch standard deviation, energy mean, energy standard deviation, etc. For HMM< vectors of delta pitch with delta-delta pitch and delta energy with delta-delta energy are used. A corpus of emotional speech data was recorded and the subjective evaluation of hte data was performed by 23 untrained listeners. The subjective recognition resutl was 56
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