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Mothers' Work Status and 17-month-olds' Productive Vocabulary.
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Accuracy of the Language Environment Analysis System Segmentation and Metrics: A Systematic Review.
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Abstract:
Purpose The Language Environment Analysis (LENA) system provides automated measures facilitating clinical and nonclinical research and interventions on language development, but there are only a few, scattered independent reports of these measures' validity. The objectives of the current systematic review were to (a) discover studies comparing LENA output with manual annotation, namely, accuracy of talker labels, as well as involving adult word counts (AWCs), conversational turn counts (CTCs), and child vocalization counts (CVCs); (b) describe them qualitatively; (c) quantitatively integrate them to assess central tendencies; and (d) quantitatively integrate them to assess potential moderators. Method Searches on Google Scholar, PubMed, Scopus, and PsycInfo were combined with expert knowledge, and interarticle citations resulting in 238 records screened and 73 records whose full text was inspected. To be included, studies must target children under the age of 18 years and report on accuracy of LENA labels (e.g., precision and/or recall) and/or AWC, CTC, or CVC (correlations and/or error metrics). Results A total of 33 studies, in 28 articles, were discovered. A qualitative review revealed most validation studies had not been peer reviewed as such and failed to report key methodology and results. Quantitative integration of the results was possible for a broad definition of recall and precision (M = 59% and 68%, respectively; N = 12-13), for AWC (mean r = .79, N = 13), CVC (mean r = .77, N = 5), and CTC (mean r = .36, N = 6). Publication bias and moderators could not be assessed meta-analytically. Conclusion Further research and improved reporting are needed in studies evaluating LENA segmentation and quantification accuracy, with work investigating CTC being particularly urgent. Supplemental Material https://osf.io/4nhms/.
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URL: https://hdl.handle.net/10161/20575
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Point, walk, talk: Links between three early milestones, from observation and parental report.
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Look who's talking: A comparison of automated and human-generated speaker tags in naturalistic day-long recordings.
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What Do North American Babies Hear? A large-scale cross-corpus analysis.
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A Collaborative Approach to Infant Research: Promoting Reproducibility, Best Practices, and Theory-Building.
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Familiarity plays a small role in noun comprehension at 12–18 months
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In: Infancy (2020)
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From babble to words: Infants’ early productions match words and objects in their environment
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In: Cogn Psychol (2020)
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Quantifying Sources of Variability in Infancy Research Using the Infant-Directed-Speech Preference
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In: Advances in Methods and Practices in Psychological Science ; 3 (2020), 1. - S. 24-52. - Sage Publishing. - ISSN 2515-2459. - eISSN 2515-2467 (2020)
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From babble to words: Infants’ early productions match words and objects in their environment
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From babble to words: Infants’ early productions match words and objects in their environment
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The Comprehension Boost in Early Word Learning: Older Infants Are Better Learners
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In: Child Dev Perspect (2020)
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Accuracy of the Language Environment Analysis System Segmentation and Metrics: A Systematic Review
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In: J Speech Lang Hear Res (2020)
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Look who’s talking: A comparison of automated and human-generated speaker tags in naturalistic daylong recordings
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In: Behav Res Methods (2020)
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Quantifying sources of variability in infancy research using the infant-directed-speech preference
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BabbleCor: A Crosslinguistic Corpus of Babble Development in Five Languages ...
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The INTERSPEECH 2019 computational paralinguistics challenge: Styrian dialects, continuous sleepiness, baby sounds & orca activity
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Mothers’ work status and 17‐month‐olds’ productive vocabulary
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Mothers’ work status and 17‐month‐olds’ productive vocabulary
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