Recent technological advances and research trends have enabled the collection and analysis of multi-hour or daylong recordings of children’s auditory environment. While this technology has allowed researchers to sample language experience from multiple contexts across the day, challenges remain with respect to how these audio recordings can or should be coded and analyzed. Daylong audio samples have the potential to transform our understanding of the language input that children encounter, but new analysis techniques may be necessary to take advantage of these new opportunities. The present work explores the linguistic content of the transcripts of three daylong recordings with the goal of understanding the content of these recordings in order to develop new ways to analyze and gain insight from these recordings.

While naturalistic daylong audio recordings of children’s auditory environments have the potential to reveal key insights about the input children receive and inform our theories of language development, it also presents various methodological hurdles. In the present work, we used three fully transcribed daylong audio recordings to investigate the challenge of manually extrapolating aggregate statistics and quantify the kinds of sampling choices daylong researchers can make. Our findings highlight sampling choices that maximize sampling from the full distribution of the day and potential tradeoffs between human effort and obtaining accuracy.