Using Imitation to Learn Infant-Adult Acoustic Mappings

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 unsupervised manner. The model requires feedback from the adult in order to select the right 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.