Emotional State Analysis Model of Humanoid Robot in Human-Computer Interaction Process

Abstract

The traditional humanoid robot dialogue system is generally based on template construction, which can make a good response in the set dialogue domain but cannot generate a good response to the content outside the domain. The rules of the dialogue system rely on manual design and lack of emotion detection of the interactive objects. In view of the shortcomings of traditional methods, this study designed an emotion analysis model based on deep neural network to detect the emotion of interactive objects and built an open-domain dialogue system of humanoid robot. In affective state analysis language processing, language coding, feature analysis, and Word2vec research are carried out. Then, an emotional state analysis model is constructed to train the emotional state of a humanoid robot, and the training results are summarized.