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Although non-profit commercial products such as LENA can provide valuable feedback to parents and early childhood educators about their children’s or student’s daily communication interactions, their cost and technology requirements put them out of reach of many families who could benefit. Over the last two decades, smartphones have become commonly used in most households irrespective of their socio-economic background. In this study, conducted during the COVID-19 pandemic, we aim to compare audio collected on LENA recorders versus smartphones available to families in an unsupervised data collection protocol. Approximately 10 hours of audio evaluated in this study was collected by three families in their homes during parent-child science book reading activities with their children. We report comparisons and found similar performance between the two audio capture devices based on their speech signal-tonoise ratio (NIST STNR) and word-error-rates calculated using automatic speech recognition (ASR) engines. Finally, we discuss implications of this study for expanding this technology to more diverse populations, limitations and future directions. 

Monitoring child development in terms of speech/language skills has a long-term impact on their overall growth. As student diversity continues to expand in US classrooms, there is a growing need to benchmark social-communication engagement, both from a teacher-student perspective, as well as student-student content. Given various challenges with direct observation, deploying speech technology will assist in extracting meaningful information for teachers. These will help teachers to identify and respond to students in need, immediately impacting their early learning and interest. This study takes a deep dive into exploring various hybrid ASR solutions for low-resource spontaneous preschool (3-5yrs) children (with & without developmental delays) speech, being involved in various activities, and interacting with teachers and peers in naturalistic classrooms. Various out-of-domain corpora over a wide and limited age range, both scripted and spontaneous were considered. Acoustic models based on factorized TDNNs infused with Attention, and both N-gram and RNN language models were considered. Results indicate that young children have significantly different/ developing articulation skills as compared to older children. Out-of-domain transcripts of interactions between young children and adults however enhance language model performance. Overall transcription of such data, including various non-linguistic markers, poses additional challenges.