基于嵌套长短期记忆网络的多模态情感检测系统

ccording to the current man-machine dialogue system, the research focus generally hopes that the robot itself has a specific \' IQ \' to obtain the information transmitted by the user, and feedback based on this. After that, in order to promote the further development of the dialogue system and make it have \' emotional quotient \', that is not only to understand the user \' s transmission information but also to respond to the actual situation, so that the machine has the ability to identify the emotional state. How to accurately identify the user \' s emotional state in the interaction process ( that is, the emotional detection of user input ) has become an indispensable and important part of the research process. In order to solve the problem of how to comprehensively and accurately detect user emotion in APP interaction mode, this paper discusses three possible input types through the investigation of multimodal emotion detection technology. Then an emotion detection technology based on nested long-term and short-term memory network is proposed. The nested long-term and short-term memory network deepens the number of long-term and short-term memory network layers, increases the internal and external network levels, and solves the problem of weak retention of longer-term memory in the original long-term and short-term memory network. The final experiment proves that the detection model proposed in this paper is improved and optimized compared with the comparison model, and the possible research directions in the future are discussed.