Using Imitation to learn Infant-Adult Acoustic Mappings

摘要

This paper discusses a model which conceptually demonstrates how infants could learn the normalization between infant-adult acoustics. The model proposes that the mapping can be inferred from the topological correspondences between the adult and infant acoustic spaces, that are clustered separately in an unsu-pervised manner. The model requires feedback from the adult in order to select the rigKt topology for clustering, which is a crucial aspect of the model. The feedback is in terms of an overall rating of the imitation effort by the infant, rather than a frame-by-frame correspondence. Using synthetic, but continuous speech data, we demonstrate that clusters, which have a good topological correspondence, are perceived to be similar by a phonetically trained listener.