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  1. Young children’s friendships fuel essential developmental outcomes (e.g., social-emotional competence) and are thought to provide even greater benefits to children with or at-risk for disabilities. Teacher and parent report and sociometric measures are commonly used to measure friendships, and ecobehavioral assessment has been used to capture its features on a momentary basis. In this proof-of-concept study, we use Ubisense, the Language ENvironmental Analysis (LENA) recorder, and advanced speech processing algorithms to capture features of friendship –child-peer speech and proximity within activity areas . We collected 12,332 1-second speech and location data points. Our preliminary results indicate the focal child at-risk for a disability and each playmate spent time vocalizing near one another across 4 activity areas. Additionally, compared to the Blocks activity area, the children had significantly lower odds of talking while in proximity during Manipulatives and Science. This suggests that the activity areas children occupy may affect their engagement with peers and, in turn, the friendships they development. The proposed approach is a groundbreaking advance to understanding and supporting children’s friendships.
  2. Understanding and assessing child verbal communication patterns is critical in facilitating effective language development. Typically speaker diarization is performed to explore children’s verbal engagement. Understanding which activity areas stimulate verbal communication can help promote more efficient language development. In this study, we present a two stage children vocal engagement prediction system that consists of (1) a near to real-time, noise robust system that measures the duration of child-to-adult and child-to-child conversations, and tracks the number of conversational turn-takings, (2) a novel child location tracking strategy, that determines in which activity areas a child spends most/least of their time. A proposed child–adult turn-taking solution relies exclusively on vocal cues observed during the interaction between a child and other children, and/or classroom teachers. By employing a threshold optimized speech activity detection using a linear combination of voicing measures, it is possible to achieve effective speech/non-speech segment detection prior to conversion assessment. This TO-COMBO-SAD reduces classification error rates for adult-child audio by 21.34% and 27.3% compared to a baseline i-Vector and standard Bayesian Information Criterion diarization systems, respectively. In addition, this study presents a unique location tracking system adult-child that helps determine the quantity of child–adult communication in specific activity areas, and whichmore »activities stimulate voice communication engagement in a child–adult education space. We observe that our proposed location tracking solution offers unique opportunities to assess speech and language interaction for children, and quantify the location context which would contribute to improve verbal communication.« less