结合数据场情感空间和混合蛙跳算法的连续语音情感变化趋势检测

针对现有的情感计算算法中存在着情感跟踪延迟的问题,且没有考虑到情感状态的连续性的情况,提出了一种结合数据场情感空间和混合蛙跳算法的连续语音情感变化趋势检测技术。首先构建数据场情感空间,利用情感特征量模拟数据场粒子,用势能函数描述粒子之间的相互作用。然后运用混合蛙跳算法技术,用青蛙个体来模拟情感状态变化过程中的情感特征量,得到情感变化的趋势。通过对变化趋势的分析,可以达到情感预测的目的。经实验证明,该算法性能比现有算法有较大改进。 

Continuous speech emotion trend detection based on data field emotion space and shuffled frog-leaping algorithm

For the existing emotional computing algorithms, there exists emotional tracking delay, and the continuity of emotional state is not taken into account. In response to this situation, this study presents a continuous speech emotion trend detection technology based on data field emotion space and shuffled frog-leaping algorithm. Firstly, the emotional space of the data field is constructed, and the data field particles are simulated by the emotional features. The interaction between the particles is described by the potential energy function. And then use the shuffled frog-leaping algorithm, with the frog individual to simulate the emotional characteristics among the emotional state changes, so as to find the trend of emotional change. Experiments show that the algorithm performance is indeed better than the existing algorithms.