Abstract
Children's early speech often bears little resemblance to adult speech in form or content, and yet caregivers often find meaning in young children's utterances. Precisely how caregivers are able to do this remains poorly understood. We propose that successful early communication (an essential building block of language development) relies not just on children's growing linguistic knowledge, but also on adults' sophisticated inferences. These inferences, we further propose, are optimized for fine-grained details of how children speak. We evaluate these ideas using a set of candidate computational models of spoken word recognition based on deep learning and Bayesian inference, which instantiate competing hypotheses regarding the information sources used by adults to understand children. We find that the best-performing models (evaluated on datasets of adult interpretations of child speech) are those that have strong prior expectations about what children are likely to want to communicate, rather than the actual phonetic contents of what children say. We further find that adults' behavior is best characterized as well-tuned to specific children: the more closely a word recognition model is tuned to the particulars of an individual child's actual linguistic behavior, the better it predicts adults' inferences about what the child has said. These results offer a comprehensive investigation into the role of caregivers as child-directed listeners, with broader consequences for theories of language acquisition.
Abstract (translated)
URL
https://arxiv.org/abs/2206.07807
