Speech Emotion Recognition Research Based on the Stacked Generalization Ensemble Neural Network for Robot Pet

In this paper, we present an emotion recognition system using the stacked generalization ensemble neural network for special human affective state in the speech signal. 450 short emotional sentences with different contents from 3 speakers were collected as experiment materials. The features relevant with energy, speech rate, pitch and formant are extracted from speech signals. Stacked generalization ensemble neural networks are used as the classifier for 5 emotions including anger, calmness, happiness, sadness and boredom. First, compared with the traditional BP network or wavelet neural network, the results of experiments show that the stacked generalization ensemble neural network has faster convergence speed and higher recognition rate. Second, after discussing the advantage and disadvantage between different ensemble neural networks, suitable decision will be made for robot pet.