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.