Incrementally emotion mapping based on GMM

Abstract:

In order to obtain users' actual emotional status effectively and promote a harmonious human-computer interaction experience, combined with the AVS emotional space and big five personality theory, this paper proposes an incremental emotion mapping model based on Gauss mixture model. First of all, with three attributes in AVS emotional space (A, V, S) coordinate, the emotional probability value and space distribution is calculated with Gauss mixture model. Secondly, based on differences of individual users, analytic hierarchy method was used to study the relationship between big five personality and emotional attributes, personalized cognitive parameters of the user was obtained, and the realization of emotional mapping results with personalized knowledge were achieved. Then, incremental learning method was applied to get real-time correction of the spatial distribution of emotion type, thus ensuring the high accuracy of emotional classification. Finally, the experimental results show that this method has a high degree of consistency with the real emotional state of the user, and also has good adaptability.