A thorough evaluation of the Language Environment Analysis (LENA) system
In the previous decade, dozens of studies involving thousands of children across several research disciplines have made use of a combined daylong audio-recorder and automated algorithmic analysis called the LENA Ⓡ system, which aims to assess children’s language environment. While the system’s preva...
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| Published in | Behavior research methods Vol. 53; no. 2; pp. 467 - 486 |
|---|---|
| Main Authors | , , , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
New York
Springer US
01.04.2021
Springer Nature B.V Psychonomic Society, Inc |
| Subjects | |
| Online Access | Get full text |
| ISSN | 1554-3528 1554-351X 1554-3528 |
| DOI | 10.3758/s13428-020-01393-5 |
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| Abstract | In the previous decade, dozens of studies involving thousands of children across several research disciplines have made use of a combined daylong audio-recorder and automated algorithmic analysis called the LENA
Ⓡ
system, which aims to assess children’s language environment. While the system’s prevalence in the language acquisition domain is steadily growing, there are only scattered validation efforts on only some of its key characteristics. Here, we assess the LENA
Ⓡ
system’s accuracy across all of its key measures: speaker classification, Child Vocalization Counts (CVC), Conversational Turn Counts (CTC), and Adult Word Counts (AWC). Our assessment is based on manual annotation of clips that have been randomly or periodically sampled out of daylong recordings, collected from (a) populations similar to the system’s original training data (North American English-learning children aged 3-36 months), (b) children learning another dialect of English (UK), and (c) slightly older children growing up in a different linguistic and socio-cultural setting (Tsimane’ learners in rural Bolivia). We find reasonably high accuracy in some measures (AWC, CVC), with more problematic levels of performance in others (CTC, precision of male adults and other children). Statistical analyses do not support the view that performance is worse for children who are dissimilar from the LENA
Ⓡ
original training set. Whether LENA
Ⓡ
results are accurate enough for a given research, educational, or clinical application depends largely on the specifics at hand. We therefore conclude with a set of recommendations to help researchers make this determination for their goals. |
|---|---|
| AbstractList | In the previous decade, dozens of studies involving thousands of children across several research disciplines have made use of a combined daylong audio-recorder and automated algorithmic analysis called the LENAⓇ system, which aims to assess children’s language environment. While the system’s prevalence in the language acquisition domain is steadily growing, there are only scattered validation efforts on only some of its key characteristics. Here, we assess the LENAⓇ system’s accuracy across all of its key measures: speaker classification, Child Vocalization Counts (CVC), Conversational Turn Counts (CTC), and Adult Word Counts (AWC). Our assessment is based on manual annotation of clips that have been randomly or periodically sampled out of daylong recordings, collected from (a) populations similar to the system’s original training data (North American English-learning children aged 3-36 months), (b) children learning another dialect of English (UK), and (c) slightly older children growing up in a different linguistic and socio-cultural setting (Tsimane’ learners in rural Bolivia). We find reasonably high accuracy in some measures (AWC, CVC), with more problematic levels of performance in others (CTC, precision of male adults and other children). Statistical analyses do not support the view that performance is worse for children who are dissimilar from the LENAⓇ original training set. Whether LENAⓇ results are accurate enough for a given research, educational, or clinical application depends largely on the specifics at hand. We therefore conclude with a set of recommendations to help researchers make this determination for their goals. In the previous decade, dozens of studies involving thousands of children across several research disciplines have made use of a combined daylong audio-recorder and automated algorithmic analysis called the LENA system, which aims to assess children's language environment. While the system's prevalence in the language acquisition domain is steadily growing, there are only scattered validation efforts on only some of its key characteristics. Here, we assess the LENA system's accuracy across all of its key measures: speaker classification, Child Vocalization Counts (CVC), Conversational Turn Counts (CTC), and Adult Word Counts (AWC). Our assessment is based on manual annotation of clips that have been randomly or periodically sampled out of daylong recordings, collected from (a) populations similar to the system's original training data (North American English-learning children aged 3-36 months), (b) children learning another dialect of English (UK), and (c) slightly older children growing up in a different linguistic and socio-cultural setting (Tsimane' learners in rural Bolivia). We find reasonably high accuracy in some measures (AWC, CVC), with more problematic levels of performance in others (CTC, precision of male adults and other children). Statistical analyses do not support the view that performance is worse for children who are dissimilar from the LENA original training set. Whether LENA results are accurate enough for a given research, educational, or clinical application depends largely on the specifics at hand. We therefore conclude with a set of recommendations to help researchers make this determination for their goals. In the previous decade, dozens of studies involving thousands of children across several research disciplines have made use of a combined daylong audio-recorder and automated algorithmic analysis called the LENAⓇ system, which aims to assess children's language environment. While the system's prevalence in the language acquisition domain is steadily growing, there are only scattered validation efforts on only some of its key characteristics. Here, we assess the LENAⓇ system's accuracy across all of its key measures: speaker classification, Child Vocalization Counts (CVC), Conversational Turn Counts (CTC), and Adult Word Counts (AWC). Our assessment is based on manual annotation of clips that have been randomly or periodically sampled out of daylong recordings, collected from (a) populations similar to the system's original training data (North American English-learning children aged 3-36 months), (b) children learning another dialect of English (UK), and (c) slightly older children growing up in a different linguistic and socio-cultural setting (Tsimane' learners in rural Bolivia). We find reasonably high accuracy in some measures (AWC, CVC), with more problematic levels of performance in others (CTC, precision of male adults and other children). Statistical analyses do not support the view that performance is worse for children who are dissimilar from the LENAⓇ original training set. Whether LENAⓇ results are accurate enough for a given research, educational, or clinical application depends largely on the specifics at hand. We therefore conclude with a set of recommendations to help researchers make this determination for their goals.In the previous decade, dozens of studies involving thousands of children across several research disciplines have made use of a combined daylong audio-recorder and automated algorithmic analysis called the LENAⓇ system, which aims to assess children's language environment. While the system's prevalence in the language acquisition domain is steadily growing, there are only scattered validation efforts on only some of its key characteristics. Here, we assess the LENAⓇ system's accuracy across all of its key measures: speaker classification, Child Vocalization Counts (CVC), Conversational Turn Counts (CTC), and Adult Word Counts (AWC). Our assessment is based on manual annotation of clips that have been randomly or periodically sampled out of daylong recordings, collected from (a) populations similar to the system's original training data (North American English-learning children aged 3-36 months), (b) children learning another dialect of English (UK), and (c) slightly older children growing up in a different linguistic and socio-cultural setting (Tsimane' learners in rural Bolivia). We find reasonably high accuracy in some measures (AWC, CVC), with more problematic levels of performance in others (CTC, precision of male adults and other children). Statistical analyses do not support the view that performance is worse for children who are dissimilar from the LENAⓇ original training set. Whether LENAⓇ results are accurate enough for a given research, educational, or clinical application depends largely on the specifics at hand. We therefore conclude with a set of recommendations to help researchers make this determination for their goals. In the previous decade, dozens of studies involving thousands of children across several research disciplines have made use of a combined daylong audio-recorder and automated algorithmic analysis called the LENA Ⓡ system, which aims to assess children’s language environment. While the system’s prevalence in the language acquisition domain is steadily growing, there are only scattered validation efforts on only some of its key characteristics. Here, we assess the LENA Ⓡ system’s accuracy across all of its key measures: speaker classification, Child Vocalization Counts (CVC), Conversational Turn Counts (CTC), and Adult Word Counts (AWC). Our assessment is based on manual annotation of clips that have been randomly or periodically sampled out of daylong recordings, collected from (a) populations similar to the system’s original training data (North American English-learning children aged 3-36 months), (b) children learning another dialect of English (UK), and (c) slightly older children growing up in a different linguistic and socio-cultural setting (Tsimane’ learners in rural Bolivia). We find reasonably high accuracy in some measures (AWC, CVC), with more problematic levels of performance in others (CTC, precision of male adults and other children). Statistical analyses do not support the view that performance is worse for children who are dissimilar from the LENA Ⓡ original training set. Whether LENA Ⓡ results are accurate enough for a given research, educational, or clinical application depends largely on the specifics at hand. We therefore conclude with a set of recommendations to help researchers make this determination for their goals. In the previous decade, dozens of studies involving thousands of children across several research disciplines have made use of a combined daylong audio-recorder and automated algorithmic analysis called the LENA system, which aims to assess children's language environment. While the system's prevalence in the language acquisition domain is steadily growing, there are only scattered validation efforts on only some of its key characteristics. Here, we assess the LENA system's accuracy across all of its key measures: speaker classification, Child Vocalization Counts (CVC), Conversational Turn Counts (CTC), and Adult Word Counts (AWC). Our assessment is based on manual annotation of clips that have been randomly or periodically sampled out of daylong recordings, collected from (a) populations similar to the system's original training data (North American English-learning children aged 3-36 months), (b) children learning another dialect of English (UK), and (c) slightly older children growing up in a different linguistic and socio-cultural setting (Tsimane' learners in rural Bolivia). We find reasonably high accuracy in some measures (AWC, CVC), with more problematic levels of performance in others (CTC, precision of male adults and other children). Statistical analyses do not support the view that performance is worse for children who are dissimilar from the LENA original training set. Whether LENA results are accurate enough for a given research, educational, or clinical application depends largely on the specifics at hand. We therefore conclude with a set of recommendations to help researchers make this determination for their goals. In the previous decade, dozens of studies involving thousands of children across several research disciplines have made use of a combined daylong audio-recorder and automated algorithmic analysis called the LENA® system, which aims to assess children’s language environment. While the system’s prevalence in the language acquisition domain is steadily growing, there are only scattered validation efforts on only some of its key characteristics. Here, we assess the LENA® system’s accuracy across all of its key measures: speaker classification, Child Vocalization Counts (CVC), Conversational Turn Counts (CTC), and Adult Word Counts (AWC). Our assessment is based on manual annotation of clips that have been randomly or periodically sampled out of daylong recordings, collected from (a) populations similar to the system’s original training data (North American English-learning children aged 3–36 months), (b) children learning another dialect of English (UK), and (c) slightly older children growing up in a different linguistic and socio-cultural setting (Tsimane’ learners in rural Bolivia). We find reasonably high accuracy in some measures (AWC, CVC), with more problematic levels of performance in others (CTC, precision of male adults and other children). Statistical analyses do not support the view that performance is worse for children who are dissimilar from the LENA® original training set. Whether LENA® results are accurate enough for a given research, educational, or clinical application depends largely on the specifics at hand. We therefore conclude with a set of recommendations to help researchers make this determination for their goals. |
| Author | Cristia, Alejandrina Bergelson, Elika Lavechin, Marvin Scaff, Camila Räsänen, Okko Soderstrom, Melanie Rowland, Caroline Bunce, John |
| AuthorAffiliation | 7 Psychology & Neuroscience, Duke University, Durham, North Carolina, USA 6 Department of Signal Processing and Acoustics, Aalto University, Espoo, Finland 2 Department of Psychology, University of Manitoba, Winnipeg, Canada 3 Donders Centre for Brain, Cognition and Behaviour, Radboud University, Nijmegen, The Netherlands 1 Laboratoire de Sciences Cognitives et de Psycholinguistique, Département d’études cognitives, ENS, EHESS, CNRS, PSL University, 29, rue d’Ulm, 75005, Paris, France 4 Max Planck Institute for Psycholinguistics, Nijmegen, The Netherlands 5 Unit of Computing Sciences, Tampere University, Tampere, Finland |
| AuthorAffiliation_xml | – name: 6 Department of Signal Processing and Acoustics, Aalto University, Espoo, Finland – name: 7 Psychology & Neuroscience, Duke University, Durham, North Carolina, USA – name: 4 Max Planck Institute for Psycholinguistics, Nijmegen, The Netherlands – name: 3 Donders Centre for Brain, Cognition and Behaviour, Radboud University, Nijmegen, The Netherlands – name: 2 Department of Psychology, University of Manitoba, Winnipeg, Canada – name: 5 Unit of Computing Sciences, Tampere University, Tampere, Finland – name: 1 Laboratoire de Sciences Cognitives et de Psycholinguistique, Département d’études cognitives, ENS, EHESS, CNRS, PSL University, 29, rue d’Ulm, 75005, Paris, France |
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| Copyright | The Psychonomic Society, Inc. 2020 The Psychonomic Society, Inc. 2020. Distributed under a Creative Commons Attribution 4.0 International License |
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| Keywords | Speech technology English Agreement Adult Word Count Child Vocalization Count Conversational Turn Count Tsimane Human transcription Method comparison Reliability Measurement error LENA |
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R Markdown: Integrating a reproducible analysis tool into introductory statistics. arXiv:1402.1894. CanaultMLe NormandMTFoudilSLoundonNThai-VanHReliability of the Language ENvironment Analysis system (LENATM) in European FrenchBehavior Research Methods20164831109112410.3758/s13428-015-0634-826174716 d’ApiceKLathamRMvon StummSA naturalistic home observational approach to children’s language, cognition, and behaviorDevelopmental Psychology20195571414142710.1037/dev000073331033308 Lamere, P., Kwok, P., Gouvea, E., Raj, B., Singh, R., Walker, W., & Wolf, P. (2003). The CMU SPHINX-4 speech recognition system. In IEEE Intl. Conf. on Acoustics, Speech and Signal Processing. Hong Kong. SeidlACristiaASoderstromMKoE-SAbelEAKellermanASchwichtenbergAInfant-mother acoustic-prosodic alignment and developmental riskJournal of Speech, Language, and Hearing Research20186161369138010.1044/2018_JSLHR-S-17-0287 Ryant, N., Church, K., Cieri, C., Cristia, A., Du, J., Ganapathy, S., & Liberman, M. (2019). Second DIHARD Challenge evaluation plan. Linguistic Data Consortium, Tech. Rep. GreenwoodCRThiemann-BourqueKWalkerDBuzhardtJGilkersonJAssessing children’s home language environments using automatic speech recognition technologyCommunication Disorders Quarterly2011322839210.1177/1525740110367826 GohK-IBarabásiA-LBurstiness and memory in complex systemsEPL (Europhysics Letters)20088144800210.1209/0295-5075/81/48002 Warlaumont, A., Pretzer, G., Walle, E., Mendoza, S., & Lopez, L. (2016). Warlaumont HomeBank corpus. Garcia-MoralAISolera-UrenaRPelaez-MorenoCDiaz-de-MariaFData balancing for efficient training of hybrid ANN/HMM automatic speech recognition systemsIEEE Transactions on Audio, Speech, and Language Processing201119346848110.1109/TASL.2010.2050513 WeislederAFernaldATalking to children mattersPsychological Science201324112143215210.1177/0956797613488145240226495510534 Soderstrom, M., Bergelson, E., Warlaumont, A., Rosemberg, C., Casillas, M., Rowland, C., & Bunce, J. (2019). The ACLEW Random Sampling corpus. Manuscript in Progress. VanDam, M., & De Palma, P. (2018). A modular, extensible approach to massive ecologically valid behavioral data. Behavior Research Methods. https://doi.org/10.3758/s13428-018-1167-8. Bergelson, E. (2016). Bergelson Seedlings Homebank corpus. https://doi.org/10.21415/T5PK6D. McDivitt, K., & Soderstrom, M. (2016). McDivitt homebank corpus. CristiaABulgarelliFBergelsonEAccuracy of the Language Environment Analysis System Segmentation and Metrics: A Systematic ReviewJournal of Speech, Language, and Hearing Research20206341093110510.1044/2020_JSLHR-19-00017 VanDam, M., Warlaumont, A. S., Bergelson, E., Cristia, A., Soderstrom, M., De Palma, P., & MacWhinney, B. (2016). HomeBank: An online repository of daylong child-centered audio recordings, (Vol. 50 pp. 1921–1932), DOI https://doi.org/10.1055/s-0036-1580745. 1393_CR29 1393_CR28 J Gilkerson (1393_CR15) 2017; 26 T Busch (1393_CR7) 2018; 50 1393_CR21 1393_CR20 1393_CR23 DK Oller (1393_CR24) 2010; 107 FJ Zimmerman (1393_CR38) 2009; 124 1393_CR22 AI Garcia-Moral (1393_CR13) 2011; 19 1393_CR25 1393_CR27 1393_CR26 E Bergelson (1393_CR3) 2019; 22 1393_CR9 1393_CR4 1393_CR5 HV Ganek (1393_CR12) 2018; 39 1393_CR2 A Seidl (1393_CR30) 2018; 61 F Bulgarelli (1393_CR6) 2019; 52 1393_CR17 K-I Goh (1393_CR18) 2008; 81 A Cristia (1393_CR10) 2020; 63 CR Greenwood (1393_CR19) 2011; 32 1393_CR32 1393_CR1 1393_CR31 1393_CR34 1393_CR33 1393_CR35 1393_CR16 J Gilkerson (1393_CR14) 2016; 85 1393_CR37 M Canault (1393_CR8) 2016; 48 K d’Apice (1393_CR11) 2019; 55 A Weisleder (1393_CR36) 2013; 24 |
| References_xml | – reference: Bredin, H. (2017). Pyannote.metrics: A toolkit for reproducible evaluation, diagnostic, and error analysis of speaker diarization systems In INTERSPEECH (pp. 3587–3591). – reference: BuschTSangenAVanpouckeFvan WieringenACorrelation and agreement between Language ENvironment Analysis (LENATM) and manual transcription for Dutch natural language recordingsBehavior Research Methods20185051921193210.3758/s13428-017-0960-028936690 – reference: VanDam, M., Warlaumont, A. S., Bergelson, E., Cristia, A., Soderstrom, M., De Palma, P., & MacWhinney, B. (2016). HomeBank: An online repository of daylong child-centered audio recordings, (Vol. 50 pp. 1921–1932), DOI https://doi.org/10.1055/s-0036-1580745. – reference: Lehet, M., Arjmandi, M. K., Dilley, L. C., Roy, S., & Houston, D. (2018). Fidelity of automatic speech processing for adult speech classifications using the Language ENvironment Analysis (LENA) system. Proceedings of Interspeech, 3–7. – reference: BulgarelliFBergelsonELook who’s talking: A comparison of automated and human-generated speaker tags in naturalistic day-long recordingsBehavior Research Methods20195264165310.3758/s13428-019-01265-7 – reference: GohK-IBarabásiA-LBurstiness and memory in complex systemsEPL (Europhysics Letters)20088144800210.1209/0295-5075/81/48002 – reference: GilkersonJRichardsJAWarrenSFMontgomeryJKGreenwoodCRKimbrough OllerDPaulTDMapping the early language environment using all-day recordings and automated analysisAmerican Journal of Speech-Language Pathology201726224810.1044/2016∖_AJSLP-15-0169284184566195063 – reference: d’ApiceKLathamRMvon StummSA naturalistic home observational approach to children’s language, cognition, and behaviorDevelopmental Psychology20195571414142710.1037/dev000073331033308 – reference: Garcia-MoralAISolera-UrenaRPelaez-MorenoCDiaz-de-MariaFData balancing for efficient training of hybrid ANN/HMM automatic speech recognition systemsIEEE Transactions on Audio, Speech, and Language Processing201119346848110.1109/TASL.2010.2050513 – reference: BergelsonECasillasMSoderstromMSeidlAWarlaumontASAmatuniAWhat do North American babies hear? A large-scale cross-corpus analysisDevelopmental Science2019221e1272410.1111/desc.1272430369005 – reference: McDivitt, K., & Soderstrom, M. (2016). McDivitt homebank corpus. – reference: Warlaumont, A., Pretzer, G., Walle, E., Mendoza, S., & Lopez, L. (2016). Warlaumont HomeBank corpus. – reference: Casillas, M., Bergelson, E., Warlaumont, A. S., Cristia, A., Soderstrom, M., VanDam, M., & Sloetjes, H. (2017). A new workflow for semi-automatized annotations: Tests with long-form naturalistic recordings of children’s language environments. In Interspeech (pp. 2098–2102). – reference: Rowland, C. F., Bidgood, A., Durrant, S., Peter, M., & Pine, J. M. (2018). The Language 0-5 Project. University of Liverpool. https://doi.org/10.17605/OSF.IO/KAU5F. – reference: Bergelson, E. (2016). Bergelson Seedlings Homebank corpus. https://doi.org/10.21415/T5PK6D. – reference: GilkersonJZhangYXuDRichardsJAXuXJiangFToppingsKEvaluating language environment analysis system performance for Chinese: A pilot study in ShanghaiJournal of Speech Language and Hearing Research201685244545210.1044/2015 – reference: WeislederAFernaldATalking to children mattersPsychological Science201324112143215210.1177/0956797613488145240226495510534 – reference: Gilkerson, J., & Richards, J. A. (2008a). The LENA Natural Language Study. LENA Foundation. – reference: CristiaABulgarelliFBergelsonEAccuracy of the Language Environment Analysis System Segmentation and Metrics: A Systematic ReviewJournal of Speech, Language, and Hearing Research20206341093110510.1044/2020_JSLHR-19-00017 – reference: Scaff, C., Stieglitz, J., Casillas, M., & Cristia, A. (2019). Daylong audio recordings of young children in a forager-farmer society show low levels of verbal input with minimal age-related change. Manuscript in Progress. – reference: Orena, A.J. (2019). Growing up bilingual: Examining the language input and word segmentation abilities of bilingual infants. PsyArXiv, https://doi.org/10.31234/osf.io/x9wr8. – reference: GreenwoodCRThiemann-BourqueKWalkerDBuzhardtJGilkersonJAssessing children’s home language environments using automatic speech recognition technologyCommunication Disorders Quarterly2011322839210.1177/1525740110367826 – reference: RStudio Team (2019). RStudio: Integrated development environment for R. Boston, MA: RStudio, Inc. Retrieved from http://www.rstudio.com/. – reference: RTeam, et al. (2013). R: a language and environment for statistical computing. Vienna. – reference: Baumer, B., Cetinkaya-Rundel, M., Bray, A., Loi, L., & Horton, N. J. (2014). R Markdown: Integrating a reproducible analysis tool into introductory statistics. arXiv:1402.1894. – reference: Gilkerson, J., Coulter, K. K., & Richards, J. A. (2008b). Transcriptional analyses of the LENA natural language corpus. LENA Foundation. – reference: VanDam, M., & De Palma, P. (2018). A modular, extensible approach to massive ecologically valid behavioral data. Behavior Research Methods. https://doi.org/10.3758/s13428-018-1167-8. – reference: Xu, D., Yapanel, U., & Gray, S. (2009). Reliability of the LENATM Language Environment Analysis System in young children’s natural home environment. LENA Foundation. – reference: Lamere, P., Kwok, P., Gouvea, E., Raj, B., Singh, R., Walker, W., & Wolf, P. (2003). The CMU SPHINX-4 speech recognition system. In IEEE Intl. Conf. on Acoustics, Speech and Signal Processing. Hong Kong. – reference: MacWhinney, B. (2017). Tools for Analyzing Talk Part 1: The CHAT Transcription Format. Carnegie. – reference: Ryant, N., Church, K., Cieri, C., Cristia, A., Du, J., Ganapathy, S., & Liberman, M. (2019). Second DIHARD Challenge evaluation plan. Linguistic Data Consortium, Tech. Rep. – reference: Bergelson, E., Cristia, A., Soderstrom, M., Warlaumont, A., Rosemberg, C., Casillas, M., & Bunce, J. (2017). ACLEW project. Databrary. – reference: CanaultMLe NormandMTFoudilSLoundonNThai-VanHReliability of the Language ENvironment Analysis system (LENATM) in European FrenchBehavior Research Methods20164831109112410.3758/s13428-015-0634-826174716 – reference: SeidlACristiaASoderstromMKoE-SAbelEAKellermanASchwichtenbergAInfant-mother acoustic-prosodic alignment and developmental riskJournal of Speech, Language, and Hearing Research20186161369138010.1044/2018_JSLHR-S-17-0287 – reference: ZimmermanFJGilkersonJRichardsJAChristakisDAXuDGraySYapanelUTeaching by listening: The importance of adult-child conversations to language developmentPediatrics2009124134234910.1542/peds.2008-2267https://doi.org/10.1542/peds.2008-2267 – reference: OllerDKNiyogiPGraySRichardsJAGilkersonJXuDCutlerEAAutomated vocal analysis of naturalistic recordings from children with autism, language delay, and typical developmentProceedings of the National Academy of Sciences201010730133541335910.1073/pnas.1003882107 – reference: Soderstrom, M., Bergelson, E., Warlaumont, A., Rosemberg, C., Casillas, M., Rowland, C., & Bunce, J. (2019). The ACLEW Random Sampling corpus. Manuscript in Progress. – reference: GanekHVEriks-BrophyAA concise protocol for the validation of Language ENvironment Analysis (LENA) conversational turn counts in VietnameseCommunication Disorders Quarterly201839237138010.1177/1525740117705094 – volume: 55 start-page: 1414 issue: 7 year: 2019 ident: 1393_CR11 publication-title: Developmental Psychology doi: 10.1037/dev0000733 – ident: 1393_CR20 – ident: 1393_CR9 doi: 10.21437/Interspeech.2017-1418 – ident: 1393_CR16 – ident: 1393_CR37 – volume: 39 start-page: 371 issue: 2 year: 2018 ident: 1393_CR12 publication-title: Communication Disorders Quarterly doi: 10.1177/1525740117705094 – volume: 85 start-page: 445 issue: 2 year: 2016 ident: 1393_CR14 publication-title: Journal of Speech Language and Hearing Research doi: 10.1044/2015 – ident: 1393_CR22 – volume: 24 start-page: 2143 issue: 11 year: 2013 ident: 1393_CR36 publication-title: Psychological Science doi: 10.1177/0956797613488145 – volume: 124 start-page: 342 issue: 1 year: 2009 ident: 1393_CR38 publication-title: Pediatrics doi: 10.1542/peds.2008-2267 – volume: 52 start-page: 641 year: 2019 ident: 1393_CR6 publication-title: Behavior Research Methods doi: 10.3758/s13428-019-01265-7 – ident: 1393_CR32 – ident: 1393_CR27 – ident: 1393_CR29 – volume: 22 start-page: e12724 issue: 1 year: 2019 ident: 1393_CR3 publication-title: Developmental Science doi: 10.1111/desc.12724 – ident: 1393_CR4 – ident: 1393_CR17 – ident: 1393_CR21 – volume: 61 start-page: 1369 issue: 6 year: 2018 ident: 1393_CR30 publication-title: Journal of Speech, Language, and Hearing Research doi: 10.1044/2018_JSLHR-S-17-0287 – volume: 50 start-page: 1921 issue: 5 year: 2018 ident: 1393_CR7 publication-title: Behavior Research Methods doi: 10.3758/s13428-017-0960-0 – ident: 1393_CR34 doi: 10.3758/s13428-018-1167-8 – ident: 1393_CR1 doi: 10.5070/T581020118 – ident: 1393_CR5 doi: 10.21437/Interspeech.2017-411 – ident: 1393_CR2 – volume: 26 start-page: 248 issue: 2 year: 2017 ident: 1393_CR15 publication-title: American Journal of Speech-Language Pathology doi: 10.1044/2016∖_AJSLP-15-0169 – ident: 1393_CR23 – ident: 1393_CR26 doi: 10.17605/OSF.IO/KAU5F – volume: 32 start-page: 83 issue: 2 year: 2011 ident: 1393_CR19 publication-title: Communication Disorders Quarterly doi: 10.1177/1525740110367826 – ident: 1393_CR31 – volume: 63 start-page: 1093 issue: 4 year: 2020 ident: 1393_CR10 publication-title: Journal of Speech, Language, and Hearing Research doi: 10.1044/2020_JSLHR-19-00017 – volume: 81 start-page: 48002 issue: 4 year: 2008 ident: 1393_CR18 publication-title: EPL (Europhysics Letters) doi: 10.1209/0295-5075/81/48002 – volume: 107 start-page: 13354 issue: 30 year: 2010 ident: 1393_CR24 publication-title: Proceedings of the National Academy of Sciences doi: 10.1073/pnas.1003882107 – ident: 1393_CR35 – ident: 1393_CR28 – ident: 1393_CR33 doi: 10.1055/s-0036-1580745 – volume: 48 start-page: 1109 issue: 3 year: 2016 ident: 1393_CR8 publication-title: Behavior Research Methods doi: 10.3758/s13428-015-0634-8 – ident: 1393_CR25 doi: 10.31234/osf.io/x9wr8 – volume: 19 start-page: 468 issue: 3 year: 2011 ident: 1393_CR13 publication-title: IEEE Transactions on Audio, Speech, and Language Processing doi: 10.1109/TASL.2010.2050513 |
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| SubjectTerms | Behavioral Science and Psychology Child Child Language Child, Preschool Children Cognitive Psychology Cognitive science Communication Educational Status Humans Infant Language Language Development Linguistics Male Psychology Speech Statistical analysis |
| Title | A thorough evaluation of the Language Environment Analysis (LENA) system |
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