Abstract
Emotion integrates different aspects of a person, including mood (current emotional state), personality, voice or speech, color around the eyes, and facial organs' movement. We are considering the mood because a person's current emotional state must always affect upcoming emotions. So behind an emotion, all these parameters are involved, and a human being can easily recognize it by seeing that face even if more than one person is there, so for the robot to make human-like emotion, all these parameters have to be considered to imitate artificial facial expression against that emotion. Most researchers working in this area still find difficulties in determining exact emotion by the robot because facial information is not always available, especially when interacting with a group of people and mimicking exact emotion that the user can effectively recognise. In our study, the loud most speeches among the people sensed by the robot and color around eyes are considered to cope with these issues. Another issue is the rise time and fall time of emotional intensity. In other words, how long should the robot keep an emotion here? An experimental approach is applied to get these values. The proposed method used an emotional speech database to recognize the human emotion using convunational neural network (CNN) and RGB patterns to mimic the emotion, which simulates an improved humanoid robot that can express emotion like human beings and give real-time responses to the user or group of users that can make more effective Human-Robot Interaction (HRI).