Look who’s talking: A comparison of automated and human-generated speaker tags in naturalistic day-long recordings

Abstract

The LENA system has revolutionized research on language acquisition, providing both a wearable device to collect day-long recordings of children’s environments, and a set of automated outputs that process, identify, and classify speech using proprietary algorithms. This output includes information about input sources (e.g., adult male, electronics). While this system has been tested across a variety of settings, here we delve deeper into validating the accuracy and reliability of LENA’s automated diarization, i.e., tags of who is talking. Specifically, we compare LENA’s output with a gold standard set of manually generated talker tags from a dataset of 88 day-long recordings, taken from 44 infants at 6 and 7 months, which includes 57,983 utterances. We compare accuracy across a range of classifications from the original Lena Technical Report, alongside a set of analyses examining classification accuracy by utterance type (e.g., declarative, singing). Consistent with previous validations, we find overall high agreement between the human and LENA-generated speaker tags for adult speech in particular, with poorer performance identifying child, overlap, noise, and electronic speech (accuracy range across all measures: 0–92%). We discuss several clear benefits of using this automated system alongside potential caveats based on the error patterns we observe, concluding with implications for research using LENA-generated speaker tags.