Establishing the reliability of metrics extracted from long-form recordings using LENA and the ACLEW pipeline.

Autor: Cristia A; Laboratoire de Sciences Cognitives et de Psycholinguistique, Département d'Etudes cognitives, ENS, EHESS, CNRS, PSL University, Paris, France. alecristia@gmail.com., Gautheron L; Laboratoire de Sciences Cognitives et de Psycholinguistique, Département d'Etudes cognitives, ENS, EHESS, CNRS, PSL University, Paris, France.; Interdisciplinary Centre for Science and Technology Studies (IZWT) Wuppertal, University of Wuppertal, Nordrhein-Westfalen, Germany., Zhang Z; School of Computer Science and Electronic Engineering, Hunan University, Changsha, Hunan, China., Schuller B; Technische Universität München, Institute for Human-Machine Communication, Munich, Germany.; Imperial College London, GLAM - Group on Language, Audio, & Music, London, UK., Scaff C; Laboratoire de Sciences Cognitives et de Psycholinguistique, Département d'Etudes cognitives, ENS, EHESS, CNRS, PSL University, Paris, France.; Human Ecology group, Institute of Evolutionary Medicine, University of Zurich, Zurich, Switzerland., Rowland C; Max Planck Institute for Psycholinguistics, Nijmegen, The Netherlands., Räsänen O; Unit of Computing Sciences, Tampere University, Tampere, Finland., Peurey L; Laboratoire de Sciences Cognitives et de Psycholinguistique, Département d'Etudes cognitives, ENS, EHESS, CNRS, PSL University, Paris, France., Lavechin M; Laboratoire de Sciences Cognitives et de Psycholinguistique, Département d'Etudes cognitives, ENS, EHESS, CNRS, PSL University, Paris, France., Havard W; Laboratoire de Sciences Cognitives et de Psycholinguistique, Département d'Etudes cognitives, ENS, EHESS, CNRS, PSL University, Paris, France.; LLL, Université d'Orléans, CNRS, Orléans, France., Fausey CM; Psychology, University of Oregon, Eugene, OR, USA., Cychosz M; Department of Hearing and Speech Sciences, University of Maryland at College Park, College Park, MD, USA., Bergelson E; Harvard University, Psychology, Cambridge, MA, USA., Anderson H; Psychology, University of Oregon, Eugene, OR, USA., Al Futaisi N; Imperial College London, GLAM - Group on Language, Audio, & Music, London, UK., Soderstrom M; University of Manitoba, Psychology, Winnipeg, MB, Canada.
Jazyk: angličtina
Zdroj: Behavior research methods [Behav Res Methods] 2024 Dec; Vol. 56 (8), pp. 8588-8607. Date of Electronic Publication: 2024 Sep 20.
DOI: 10.3758/s13428-024-02493-2
Abstrakt: Long-form audio recordings are increasingly used to study individual variation, group differences, and many other topics in theoretical and applied fields of developmental science, particularly for the description of children's language input (typically speech from adults) and children's language output (ranging from babble to sentences). The proprietary LENA software has been available for over a decade, and with it, users have come to rely on derived metrics like adult word count (AWC) and child vocalization counts (CVC), which have also more recently been derived using an open-source alternative, the ACLEW pipeline. Yet, there is relatively little work assessing the reliability of long-form metrics in terms of the stability of individual differences across time. Filling this gap, we analyzed eight spoken-language datasets: four from North American English-learning infants, and one each from British English-, French-, American English-/Spanish-, and Quechua-/Spanish-learning infants. The audio data were analyzed using two types of processing software: LENA and the ACLEW open-source pipeline. When all corpora were included, we found relatively low to moderate reliability (across multiple recordings, intraclass correlation coefficient attributed to the child identity [Child ICC], was < 50% for most metrics). There were few differences between the two pipelines. Exploratory analyses suggested some differences as a function of child age and corpora. These findings suggest that, while reliability is likely sufficient for various group-level analyses, caution is needed when using either LENA or ACLEW tools to study individual variation. We also encourage improvement of extant tools, specifically targeting accurate measurement of individual variation.
(© 2024. The Psychonomic Society, Inc.)
Databáze: MEDLINE
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