ArticleBehavior research methods2020
Look who's talking: A comparison of automated and human-generated speaker tags in naturalistic day-long recordings.
Article in Behavior research methods, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers, 1 of them a synthesis that pooled it.
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Who cites it
22 citing papers in PubMed, 1 synthesis or guideline pooled it, 47 citations in OpenAlex.
- Accuracy of the Language Environment Analysis System Segmentation and Metrics: A Systematic Review.Journal of speech, language, and hearing research : JSLHR · 2020Pooled it
- Early child measures outpredict input measures of preschool language skills in U.S. English learners.Developmental psychology · 2026Article
- Dynamic Interaction of Affect and Language in Children's Home Environments.Developmental science · 2026Article
- Linking acoustic variability in the infants' input to their early word production.Developmental science · 2024Article
- Preliminary Technical Validation of LittleBeats™: A Multimodal Sensing Platform to Capture Cardiac Physiology, Motion, and Vocalizations.Sensors (Basel, Switzerland) · 2024Article
- Language Experience during Infancy Predicts White Matter Myelination at Age 2 Years.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2023Article
- Talking to talkers: Infants' talk status, but not their gender, is related to language input.Child development · 2023Article
- Automated measurement: The need for a more objective view of the speech and language of autistic children.Frontiers in human neuroscience · 2023Article
- Infants who experience more adult-initiated conversations have better expressive language in toddlerhood.Infancy : the official journal of the International Society on Infant Studies · 2022Article
- Everyday Parameters for Episode-to-Episode Dynamics in the Daily Music of Infancy.Cognitive science · 2022Article
- Measuring naturalistic proximity as a window into caregiver-child interaction patterns.Behavior research methods · 2022Article
- Conversation Initiation of Mothers, Fathers, and Toddlers in their Natural Home Environment.Computer speech & language · 2022Article
- Talker variability shapes early word representations in English-learning 8-month-olds.Infancy : the official journal of the International Society on Infant Studies · 2022Article
- The home environment and its relation to vocalizations in the first year of life.Pediatric medicine (Hong Kong, China) · 2022Article
- Language input in late infancy scaffolds emergent literacy skills and predicts reading related white matter development.Frontiers in human neuroscience · 2022Article
- Using a Developmental-Ecological Approach to Understand the Relation Between Language and Music.Frontiers in psychology · 2022Article
- Article
- Media exposure and language experience: Examining associations from home observations in Mexican immigrant families in the US.Infant behavior & development · 2021Article
- A thorough evaluation of the Language Environment Analysis (LENA) system.Behavior research methods · 2021Article
- Quantifying Talker Variability in North-American Infants' Daily Input.Cognitive science · 2021Article
Corrections and comments
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Authors and funding
2 authors at 1 institution in 1 country.
Funding
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.