{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,24]],"date-time":"2026-09-24T14:02:35Z","timestamp":1790258555430,"version":"4.1.0"},"reference-count":56,"publisher":"Wiley","issue":"5","license":[{"start":{"date-parts":[[2026,7,8]],"date-time":"2026-07-08T00:00:00Z","timestamp":1783468800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2026,7,8]],"date-time":"2026-07-08T00:00:00Z","timestamp":1783468800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"funder":[{"DOI":"10.13039\/100000893","name":"Simons Foundation","doi-asserted-by":"publisher","award":["034070\u201000033"],"award-info":[{"award-number":["034070\u201000033"]}],"id":[{"id":"10.13039\/100000893","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100007719","name":"University of East London","doi-asserted-by":"publisher","award":["CA22111"],"award-info":[{"award-number":["CA22111"]}],"id":[{"id":"10.13039\/100007719","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100007719","name":"University of East London","doi-asserted-by":"publisher","award":["K23MH111955"],"award-info":[{"award-number":["K23MH111955"]}],"id":[{"id":"10.13039\/100007719","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100007719","name":"University of East London","doi-asserted-by":"publisher","award":["F31DC018219"],"award-info":[{"award-number":["F31DC018219"]}],"id":[{"id":"10.13039\/100007719","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Developmental Science"],"published-print":{"date-parts":[[2026,9]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:sec>\n                    <jats:label\/>\n                    <jats:p>\n                      Wearable recorders are used in research and clinical practice to collect and measure children's vocalizations and the language environment in which they occur. Recordings generate vast amounts of audio, making manual analysis impractical and requiring automated processing. Two automated algorithms have emerged: the proprietary LENA (Language ENvironment Analysis) and the open\u2010source ACLEW (Analyzing Child Language Experiences around the World) systems; yet, systematic performance comparisons remain scarce. Here, we validate and compare the performance of these two algorithms across key measures: audio segmentation into speaker categories, conversational turn count (CTC), adult word count (AWC), and child vocalization count (CVC). This analysis is based on 25 h of manually annotated audio recordings from 50 age\u2010matched U.S. children with diverse neurodevelopmental profiles: children with Down syndrome, Fragile X syndrome, and Angelman syndrome, children at elevated likelihood of autism, and low\u2010risk controls. We hypothesized that the algorithms might be less accurate for children with neurodevelopmental conditions, since these children often show different patterns of volubility and vocal maturity compared to the typically developing children used to train the algorithms. Thus, we assessed the performance of algorithms across diagnostic groups, a crucial validation step for both cross\u2010population research and the evaluation of language interventions. Results reveal that while algorithms achieve similar performance across groups, they show different patterns: LENA makes fewer segmentation mistakes but misses many segments (identification error rate = 81.3%, percent correct = 45.3%), while ACLEW shows the opposite pattern (identification error rate = 129.4%, percent correct = 69.4%). Both LENA and ACLEW achieve reasonable levels of accuracy in their automatic counts (Pearson's\n                      <jats:italic>r<\/jats:italic>\n                      ranging from 0.78 to 0.92) and maintain stable performance across diagnostic groups. We conclude with recommendations for the validation and potential use of these algorithms in research and clinical practice.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Summary<\/jats:title>\n                    <jats:p>\n                      <jats:list list-type=\"bullet\">\n                        <jats:list-item>\n                          <jats:p>Our comparison of the LENA and ACLEW algorithms in analyzing children's language environment and vocal production across five neurodevelopmental profiles reveals similar performance but distinct error patterns.<\/jats:p>\n                        <\/jats:list-item>\n                        <jats:list-item>\n                          <jats:p>LENA makes fewer errors but misses speech (81.3% error rate, 45.3% correct); ACLEW makes more errors but captures more speech (129.4% error rate, 69.4% correct).<\/jats:p>\n                        <\/jats:list-item>\n                        <jats:list-item>\n                          <jats:p>Both algorithms maintain consistent performance across diagnostic groups, supporting their reliability for research with these 2\u2010year\u2010old populations with diverse neurodevelopmental profiles.<\/jats:p>\n                        <\/jats:list-item>\n                        <jats:list-item>\n                          <jats:p>Variations in algorithm performance are primarily driven by the total speaking time of surrounding speakers (other children and adults), rather than the diagnostic group.<\/jats:p>\n                        <\/jats:list-item>\n                      <\/jats:list>\n                    <\/jats:p>\n                  <\/jats:sec>","DOI":"10.1111\/desc.70239","type":"journal-article","created":{"date-parts":[[2026,7,8]],"date-time":"2026-07-08T10:08:12Z","timestamp":1783505292000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["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"],"prefix":"10.1111","volume":"29","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6005-9368","authenticated-orcid":false,"given":"Marvin","family":"Lavechin","sequence":"first","affiliation":[{"name":"Department of Brain &amp; Cognitive Sciences Massachusetts Institute of Technology  Cambridge Massachusetts USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8372-5837","authenticated-orcid":false,"given":"Lisa R.","family":"Hamrick","sequence":"additional","affiliation":[{"name":"Department of Psychology University of South Carolina  Columbia South Carolina USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bridgette","family":"Kelleher","sequence":"additional","affiliation":[{"name":"Department of Psychological Sciences Purdue University  West Lafayette Indiana USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Amanda","family":"Seidl","sequence":"additional","affiliation":[{"name":"Department of Communication Sciences &amp; Disorders University of Delaware  Newark Delaware USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2026,7,8]]},"reference":[{"key":"e_1_2_13_2_1","doi-asserted-by":"crossref","unstructured":"Al Futaisi N. Z.Zhang A.Cristia A.Warlaumont andB.Schuller.2019. \u201cVCMNet: Weakly Supervised Learning for Automatic Infant Vocalisation Maturity Analysis.\u201d In2019 International Conference on Multimodal Interaction 205\u2013209.https:\/\/doi.org\/10.1145\/3340555.3353751.","DOI":"10.1145\/3340555.3353751"},{"key":"e_1_2_13_3_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10803\u2010017\u20103033\u20104"},{"key":"e_1_2_13_4_1","doi-asserted-by":"publisher","DOI":"10.1037\/0012\u20101649.19.3.322"},{"key":"e_1_2_13_5_1","doi-asserted-by":"crossref","unstructured":"Bredin H.2017. \u201cpyannote.metrics: A Toolkit for Reproducible Evaluation Diagnostic and Error Analysis of Speaker Diarization Systems.\u201d InInterspeech 3587\u20133591.https:\/\/doi.org\/10.21437\/Interspeech.2017\u2010411.","DOI":"10.21437\/Interspeech.2017-411"},{"key":"e_1_2_13_6_1","doi-asserted-by":"publisher","DOI":"10.1044\/2018_AJSLP\u201017\u20100016"},{"key":"e_1_2_13_7_1","doi-asserted-by":"publisher","DOI":"10.1017\/S0305000920000525"},{"key":"e_1_2_13_8_1","doi-asserted-by":"publisher","DOI":"10.1056\/NEJMra1706537"},{"key":"e_1_2_13_9_1","doi-asserted-by":"publisher","DOI":"10.3758\/s13428\u2010017\u20100960\u20100"},{"key":"e_1_2_13_10_1","doi-asserted-by":"publisher","DOI":"10.3758\/s13428\u2010015\u20100634\u20108"},{"key":"e_1_2_13_11_1","doi-asserted-by":"crossref","unstructured":"Casillas M. E.Bergelson A. S.Warlaumont et\u00a0al.2017. \u201cA New Workflow for Semi\u2010Automatized Annotations: Tests With Long\u2010Form Naturalistic Recordings of Childrens Language Environments.\u201d InInterspeech 2017 2098\u20132102.https:\/\/doi.org\/10.21437\/Interspeech.2017\u20101418.","DOI":"10.21437\/Interspeech.2017-1418"},{"key":"e_1_2_13_12_1","doi-asserted-by":"publisher","DOI":"10.1525\/collabra.209"},{"key":"e_1_2_13_13_1","unstructured":"Charlot T. T.Kunze M.Poli A.Cristia E.Dupoux andM.Lavechin.2025. \u201cBabyHuBERT: Multilingual Self\u2010Supervised Learning for Segmenting Speakers in Child\u2010Centered Long\u2010Form Recordings (Version 1).\u201d arXiv.https:\/\/doi.org\/10.48550\/ARXIV.2509.15001."},{"key":"e_1_2_13_14_1","doi-asserted-by":"publisher","DOI":"10.1044\/2020_JSLHR\u201019\u201000017"},{"key":"e_1_2_13_15_1","doi-asserted-by":"publisher","DOI":"10.3758\/s13428\u2010020\u201001393\u20105"},{"key":"e_1_2_13_16_1","doi-asserted-by":"publisher","DOI":"10.3758\/s13428-020-01365-9"},{"key":"e_1_2_13_17_1","volume-title":"Angelman Syndrome","author":"Dagli A. 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