Abstract:

In view of the fact that the existing emotion model only divides the emotional state from the space with the interaction between emotions neglected. Therefore, this paper proposes a deep emotion association model which combines the multi-layer Restricted Boltzmann Machine with the emotion interactive cognitive network. In this model, the weights trained by the multi-layer Restricted Boltzmann Machine are used as the weights between the inputs and outputs of the correlated cognitive network. The reciprocal of the emotional space distance in the three-dimensional PAD emotional model is used as the correlation degree between emotional categories. The result of emotion classification is obtained by training the interactive cognitive network. The three basic emotions of “happy”, “angry” and “neutral” in TYUT1.0 and CASIA emotional speech database are selected as data sources, and the Deep Belief Network and deep emotional association model are used for emotional recognition. Experimental results show that the average recognition rate of the deep emotional association model is 6.06% higher than that of the Deep Belief Network. It has a better emotional recognition performance. The results prove that the deep emotion association model has strong superiority and universality in speech emotion recognition, and can reflect the interaction between emotions well.

Key words: restricted Boltzmann machine, interactive cognitive network, deep affective interaction model, emotion recognition