Open AccessAccuracy of the Language Environment Analysis System Segmentation and Metrics: A Systematic Review
Author(s) -
Alejandrina Cristià,
Federica Bulgarelli,
Elika Bergelson
Publication year - 2020
Publication title -
journal of speech, language, and hearing research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.958
H-Index - 138
eISSN - 1558-9102
pISSN - 1092-4388
DOI - 10.1044/2020_jslhr-19-00017
Subject(s) - psycinfo , scopus , natural language processing , recall , computer science , psychology , precision and recall , text segmentation , psychological intervention , artificial intelligence , medline , applied psychology , information retrieval , segmentation , cognitive psychology , psychiatry , political science , law
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 A qualitative review revealed most validation studies had not been peer reviewed as such and failed to report key methodology and results.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/.