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Speech and language development in children is crucial for ensuring optimal outcomes in their long term development and life-long educational journey. A child’s vocabulary size at the time of kindergarten entry is an early indicator of learning to read and potential long-term success in school. The preschool classroom is thus a promising venue for monitoring growth in young children by measuring their interactions with teachers and classmates. Automatic Speech Recognition (ASR) technologies provide the ability for ‘Early Childhood’ researchers for automatically analyzing naturalistic recordings in these settings. For this purpose, data are collected in a high-quality childcare center in the United States using Language Environment Analysis (LENA) devices worn by the preschool children. A preliminary task for ASR of daylong audio recordings would involve diarization, i.e., segmenting speech into smaller parts for identifying ‘who spoke when.’ This study investigates a Deep Learning-based diarization system for classroom interactions of 3-5-year-old children. However, the focus is on ’speaker group’ diarization, which includes classifying speech segments as being from adults or children from across multiple classrooms. SincNet based diarization systems achieve utterance level Diarization Error Rate of 19.1%. Utterance level speaker group confusion matrices also show promising, balanced results. These diarization systems have potential applications in developing metrics for adult-to-child or child-to-child rapid conversational turns in a naturalistic noisy early childhood setting. Such technical advancements will also help teachers better and more efficiently quantify and understand their interactions with children, make changes as needed, and monitor the impact of those changes. 

A key goal of Next Generation Science Standards is to promote interest and exploration of natural phenomena. In preschool settings, teachers prompt exploration by asking questions, encouraging informal exploration and experimentation. To date, live or offline video observation has been the sole way to capture the quality of teacher question asking in the pre-k classroom (e.g., Sanders et al., 2016). To date, Automatic Speech Recognition (ASR) has not been used to measure the content/quality of teacher talk. Here, we used ASR to quantify preschool teachers’ use of keywords that promote student exploration and inquiry.