Applying an Automatic Classifier for Child‐Directed Speech to Intervention Research: A Reanalysis

Parental language input is a key predictor of child language achievement. Parentese is a widely used style of child-directed speech (CDS) distinguished by a higher pitch and larger pitch range. A recent parent coaching randomized control trial (Parentese-RCT) demonstrated that English-speaking US parents who were coached to use parentese with their 6- to 18-month-olds increased the frequency of its use; their children showed enhanced language outcomes at 18 months. While these results are exciting, a roadblock in scaling this intervention is the fact that parentese has to be manually identified from daylong LENA recordings. Here we demonstrate that a newly developed CDS classifier captures the effect of this Parentese-RCT without human annotation, through a new variable: Proportion of CDS relative to all adult speech (CDS-Proportion). Two daylong recordings per child (N = 70) per timepoint (child age: 6, 10, 14, and 18 months) from the Parentese-RCT were re-analyzed by removing periods of sleep and estimating CDS-Proportion through the classifier. As was the case for parentese in the Parentese-RCT, CDS-Proportion was significantly enhanced in the intervention group. Moreover, the change over time in CDS-Proportion was significantly and positively correlated with infants' word use at 18 months. We emphasize that the classifier does not "recognize parentese." Likewise, CDS-Proportion is not a proxy for parentese, but rather, a variable related to parentese in complex ways. The present findings suggest a promising future for scalability of interventions using daylong recordings in combination with novel technologies. SUMMARY: Language interventions promote verbal engagement between caregivers and children; however, to be effective, these interventions need to be scalable. Language interventions have relied on manual (human) annotation to identify parental language behaviors that are linked to children's outcomes. An automated classifier for Child-Directed-Speech was applied to capture the effect of a previously published parent-coaching intervention, without human annotation. Novel technologies have the potential to enhance the scalability of parent interventions that focus on social language behaviors.