Emergence of the use of pronouns and names in triadic human-robot spoken interaction

Abstract

We present here a system capable of learning to extract the correct comprehension and production of personal pronouns and proper nouns during Human-Robot or Human-Human interactions. We use external 3D spatial and acoustic sensors with the robot iCub to allow the system to learn the proper mapping between different pronouns and names to their properties in different interaction contexts. The properties are Subject (Su), Speaker (Sp), Addressee (Ad) and Agent (Ag). A fast mapping system is used to extract correlation between the different properties. After a learning phase, the robot is able to find the missing property when only 3 out of 4 are known, or at least to discriminate which word cannot be used to be the lacking property. We present results from a set of experiments that provide some insight into aspects of human development.