情感维度下的深度情感关联模型

鉴于现有的情感模型只是从空间上对情感状态进行划分,忽略了情感之间的相互作用问题,建立了一种将多层限制玻尔兹曼机和情感关联认知网络相结合的深度情感关联模型。该模型将多层限制玻尔兹曼机训练得到的权值作为关联认知网络输入输出之间的权值,以三维情感模型中情感空间距离的倒数作为情感类别之间的关联度,通过训练关联认知网络得到最终的情感分类结果。选用TYUT1.0和CASIA情感语音库中的“高兴”“生气”和“中性”三种基本情感作为数据来源,分别采用深度信念网络和深度情感关联模型进行实验对比。实验结果显示,所构建的深度情感关联模型比深度信念网络的平均识别率最高高出6.06%,该模型得到了较好的识别结果。结果表明, 深度情感关联模型在语音情感识别上有较强的优越性和普适性,可以很好地反映情感之间的相互作用。

Model of deep affective interaction in the emotional dimension

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.