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Regardless of interaction with less frequent imitative caregivers, infants can obtain the vowels of the caregivers' mother tongues by finding the correspondence between their own vowels and the caregivers' vowels. This paper proposes a learning method based on auto-mirroring bias (AMB) with a self-evaluation mechanism to find such correspondence. AMB is the robot's anticipation of being imitated by its caregiver and has a role of narrowing the candidates for the correspondence. The self-evaluation mechanism biased by AMB works as outlier (incorrect mapping) rejection, expecting that the outliers appear less consistently than the correct mappings do in the interaction. Results from several computer simulations with real sound wave recording from a human experimenter show that the robot could successfully achieve being imitated by the proposed method even if interacting with a caregiver who would seldomly imitate its utterances.

It is a formidable issue how robots can show behaviors to be considered as the corresponding human's ones since the body structure is different between robots and humans. As a simple case for such a correspondence problem, this paper presents a robot that learns to vocalize vowels through the interaction with its caregiver. Inspired by the findings of developmental psychology, we focus on the roles of maternal imitation (i.e., imitation of the robot voices by the caregiver) since it could play a role to instruct the correspondence of the sounds. Furthermore, we suppose that it causes unconscious anchoring in which the imitated voice by the caregiver is performed unconsciously, closely to one of his/her own vowels and hereby helps to make the robot's utterances to be more vowel-like. We propose a method for vowel learning with an imitative caregiver, under the assumptions that the robot knows the desired categories of the caregiver's vowels, and the rough estimate of mapping between the region of sounds th...