Towards social integration of humanoid robots by conversational concept learning

Several real world applications of humanoids in general will require continuous service over a long time period. A humanoid robot operating in different environments over a long period of time means that A) there will be a lot of variation in the speech it has to ground semantically and B) it has to know when a conversation is of interest in order to respond. Detailed natural speech understanding is hard in real scenarios with arbitrary domains. To prepare the ground for in-domain dialogs in real day-to-day life open domain scenarios we focus on an intermediate attention level based on conversation concept listening and learning. With the aid of explicit semantic analysis new concepts from open domain conversational speech are learned together with how to react to them according to human needs. This can entail how the robot performs actions such as positioning and privacy filtering. The corresponding attention model is investigated in terms of concept error rate and word error rate using speech recordings of household conversations.