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Abstract
This study examined the communication complexity across school activities of three preverbal preschoolers with autism spectrum disorder (ASD). The three children wore Language Environment Analysis (LENA) Digital Language Processor (DLP) vests in their self-contained classroom throughout three school days. The LENA system recorded and analyzed through specialized speech algorithms the childs language environment. Identified segments of time that contained the most child vocalizations (CVs) per class activity (i.e., lunch, small group, large group, recess, and direct instruction) were subsequently manually coded for utterance type using a classification system assigning levels of complexity to the vocalizations. It was found that no one activity type elicited more frequent or complex preverbal communication for all participants, but specific individual speech profiles were identified. Future research replicating these and extending these procedures could support assessment and intervention planning for preschool students who are minimally verbal.