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This paper discusses an utterance system based on the associative memory of partner robots developed through interaction with people. Human interaction based on gestures is quite important to the expression of natural communication, and the meaning of gestures can be understood through intentional interactions with a human. We therefore propose a method for associative learning based on intentional interaction and conversation that can realize such natural communication. Steady-state genetic algorithms (SSGA) are applied in order to detect the human face and objects via image processing. Spiking neural networks are applied in order to memorize the spatio-temporal patterns of human hand motions and various relationships among the perceptual information that is conveyed. The experimental results show that the proposed method can refine the relationships among this varied perceptual information that can then inform an updated relationship to natural communication with a human. We also present methods of assisting memory and assessing a human's state.