ArticleBehavior research methods2021
ALICE: An open-source tool for automatic measurement of phoneme, syllable, and word counts from child-centered daylong recordings.
Article in Behavior research methods, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 2 of them syntheses that pooled it.
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Who cites it
18 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- From data to discovery: Technology propels speech-language research and theory-building in developmental science.Neuroscience and biobehavioral reviews · 2025Pooled it
- The use of Language ENvironment Analysis in autism research: A systematic review.Autism : the international journal of research and practice · 2025Pooled it
- Toolkit for acoustic-phonetic analysis of naturalistic speech data.Behavior research methods · 2026Article
- Performance and Biases of the LENA and ACLEW Algorithms in Analyzing Language Environments in Down, Fragile X, Angelman Syndromes, and Populations at Elevated Likelihood for Autism.Developmental science · 2026Article
- Theoretical and Methodological Insights From Five Transcribed and Annotated Daylong Audio Recordings of Young Children's Language Environments.Perspectives on psychological science : a journal of the Association for Psychological Science · 2026Article
- Thinking Critically About Algorithms for Automated Detection of Behavior: 11 Guidelines for Social and Behavioral Scientists.Developmental science · 2026Article
- Making Machine Learning Accessible for Developmental Science: The Case of Automated Face Detection.Developmental science · 2026Article
- Automated Analysis of Naturalistic Recordings in Early Childhood: Applications, Challenges, and Opportunities.IEEE signal processing magazine · 2025Article
- Using Language Environment Analysis System (LENA) in Natural Settings to Characterize Outcomes of Pivotal Response Treatment.Journal of autism and developmental disorders · 2025Article
- Artificial intelligence-powered 3D analysis of video-based caregiver-child interactions.Science advances · 2025Article
- Validation of the Language ENvironment Analysis (LENA) Automated Speech Processing Algorithm Labels for Adult and Child Segments in a Sample of Families From India.Journal of speech, language, and hearing research : JSLHR · 2025Article
- Establishing the reliability of metrics extracted from long-form recordings using LENA and the ACLEW pipeline.Behavior research methods · 2024Article
- Evaluating the accuracy of automated processing of child and adult language production in preschool classrooms.Frontiers in psychology · 2024Article
- Conversation Initiation of Mothers, Fathers, and Toddlers in their Natural Home Environment.Computer speech & language · 2022Article
- Daylong Mobile Audio Recordings Reveal Multitimescale Dynamics in Infants' Vocal Productions and Auditory Experiences.Current directions in psychological science · 2022Article
- Computational approaches to understanding interaction and development.Advances in child development and behavior · 2022Article
- Investigating Children's Interactions in Preschool Classrooms: An Overview of Research Using Automated Sensing Technologies.Early childhood research quarterlyArticle
- Open Science Considerations for Descriptive Research in Developmental Science.Infant and child developmentArticle
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5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Recordings captured by wearable microphones are a standard method for investigating young children's language environments. A key measure to quantify from such data is the amount of speech present in children's home environments. To this end, the LENA recorder and software-a popular system for measuring linguistic input-estimates the number of adult words that children may hear over the course of a recording. However, word count estimation is challenging to do in a language- independent manner; the relationship between observable acoustic patterns and language-specific lexical entities is far from uniform across human languages. In this paper, we ask whether some alternative linguistic units, namely phone(me)s or syllables, could be measured instead of, or in parallel with, words in order to achieve improved cross-linguistic applicability and comparability of an automated system for measuring child language input. We discuss the advantages and disadvantages of measuring different units from theoretical and technical points of view. We also investigate the practical applicability of measuring such units using a novel system called Automatic LInguistic unit Count Estimator (ALICE) together with audio from seven child-centered daylong audio corpora from diverse cultural and linguistic environments. We show that language-independent measurement of phoneme counts is somewhat more accurate than syllables or words, but all three are highly correlated with human annotations on the same data. We share an open-source implementation of ALICE for use by the language research community, enabling automatic phoneme, syllable, and word count estimation from child-centered audio recordings.
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