Child-directed Listening: How Caregiver Inference Enables Children's Early Verbal Communication

TL;DR

This paper demonstrates that adults use specialized inferential processes, guided by prior expectations tailored to child language, to understand children's speech despite its noisiness, impacting theories of language acquisition.

Contribution

It introduces Bayesian models showing that adult comprehension of child speech relies on environment-specific priors, a novel insight into child-directed listening.

Findings

Adult inference models predict understanding of child speech.

Prior expectations fitted to child language outperform typical adult language priors.

Child-directed listening influences assessments of children's linguistic proficiency.

Abstract

How do adults understand children's speech? Children's productions over the course of language development often bear little resemblance to typical adult pronunciations, yet caregivers nonetheless reliably recover meaning from them. Here, we employ a suite of Bayesian models of spoken word recognition to understand how adults overcome the noisiness of child language, showing that communicative success between children and adults relies heavily on adult inferential processes. By evaluating competing models on phonetically-annotated corpora, we show that adults' recovered meanings are best predicted by prior expectations fitted specifically to the child language environment, rather than to typical adult-adult language. After quantifying the contribution of this "child-directed listening" over developmental time, we discuss the consequences for theories of language acquisition, as well as the implications for commonly-used methods for assessing children's linguistic proficiency.