PROJECT SUMMARY Predicting language outcomes in children referred for early language concerns is a longstanding clinical and scientific challenge that often results in the delayed implementation of intervention. The overarching hypothesis of this project is that early motor skills—particularly the relatively under-studied domain of speech motor skills— are significantly associated with language skills in late talkers and children with typical language development and can serve as predictors of language outcomes. We will examine relationships among speech, motor, and language skills in 24-month-old children with and without delayed language development (Aim 1); furthermore, in the delayed language development group, we will examine relations between speech and motor skills at age 24 months and subsequent expressive language skills at age 36 months (Aim 2). To accomplish these goals, we will derive a variety of acoustic speech features (to quantify speech precision, consistency, coordination, speed, and rate) and language features (to quantify lexical-semantic, morphosyntactic, and phonological development) from audio and video recordings of children participating in a semi-structured play session and a speech elicitation task. Gross and fine motor skills will also be measured using the Mullen Scales of Early Learning. Machine learning techniques will then be used to integrate multimethod data (i.e., speech, motor, and language variables) in examining predictive relationships across domains. The results of this study will directly inform our understanding of mechanisms underlying communication development and dysfunction and advance a precision medicine approach to clinical decision-making. Successful completion of the project will (1) yield currently unavailable and clinically significant information regarding language development and its association with motor development in children with and without an early language delay; and (2) support the development of valid, clinically feasible assessment and prognostic procedures. The candidate is a pediatric speech-language pathologist and clinical researcher with expertise in the instrumental assessment of children’s speech. The proposed F32 application will provide the candidate with the skills needed to investigate speech, motor, and language development in pediatric populations through direct training in (1) the collection, analysis, and interpretation of developmental gross and fine motor data; (2) the collection, analysis, and interpretation of language features derived from natural language samples; and (3) advanced quantitative methodologies related to motor analysis, language analysis, and the analysis of complex data.