Finding the Correspondence of Caregiver's Vowel Categories Based on Unconscious Anchoring in Maternal Imitation

Abstract

Abstract-Due to differences in body structure between robots and humans, it is a formidable task for robots to show behaviors that correspond to human behaviors. As a simple case of this correspondence problem, this paper presents a robot that learns to vocalize vowels through interaction with its caregiver. Inspired by the findings in developmental psychology, we focus on the role of maternal imitation (i.e., imitation of a robot voice by a caregiver), which could play a role in guiding the correspondence of sounds. Furthermore, we suppose that it causes unconscious anchoring in which the imitated voice by the caregiver is approaching to one of his/her own vowels without his/her intension, and thereby works for guiding robot's utterances to be more vowel-like. We propose a method for vowel learning with an imitative caregiver under the assumption that the robot knows the desired categories of caregiver's vowels and the rough estimate of mapping between the region of sounds that the caregiver can generate and the region that the robot can generate. Through experiments with a Japanese imitative caregiver, we show that a robot succeeds in acquiring more vowel-like utterances than would be possible without such a caregiver, even when the robot is provided different mapping functions.