2005 Special Issue: Emotion recognition in human-computer interaction
Neural Networks - Special issue: Emotion and brain
Classification of acoustic events using SVM-based clustering schemes
Pattern Recognition
Applying an analysis of acted vocal emotions to improve the simulation of synthetic speech
Computer Speech and Language
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In this paper, we present an emotion recognition system using wavelet neural network and BP neural network for special human affective state in the speech signal. 750 short emotional sentences with different contents from 5 speakers were collected as experiment materials. The features relevant with energy, speech rate, pitch and formant are extracted from speech signals. Neural network are used as the classifier for 5 emotions including anger, calmness, happiness, sadness and boredom. Compared with the traditional BP network, the results of experiments show that the wavelet neural network has faster convergence speed and higher recognition rate.