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How much does prosody help word segmentation? A simulation study on infant-directed speech
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In: ISSN: 0010-0277 ; EISSN: 1873-7838 ; Cognition ; https://hal.archives-ouvertes.fr/hal-03498888 ; Cognition, Elsevier, 2022, 219, pp.104961. ⟨10.1016/j.cognition.2021.104961⟩ (2022)
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Supplementary materials: Factors structuring lexical development in toddlers: The effects of parental education, language exposure, and age ...
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Is there a bilingual disadvantage for word segmentation? A computational modeling approach
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In: ISSN: 0305-0009 ; EISSN: 1469-7602 ; Journal of Child Language ; https://hal.archives-ouvertes.fr/hal-03498905 ; Journal of Child Language, Cambridge University Press (CUP), 2021, pp.1-28. ⟨10.1017/S0305000921000568⟩ (2021)
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SCALa: A blueprint for computational models of language acquisition in social context
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In: ISSN: 0010-0277 ; EISSN: 1873-7838 ; Cognition ; https://hal.inria.fr/hal-03373586 ; Cognition, Elsevier, 2021, 213, pp.104779. ⟨10.1016/j.cognition.2021.104779⟩ (2021)
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Vocal development in a large‐scale crosslinguistic corpus
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In: ISSN: 1363-755X ; EISSN: 1467-7687 ; Developmental Science ; https://hal.archives-ouvertes.fr/hal-03498978 ; Developmental Science, Wiley, 2021, 24 (5), ⟨10.1111/desc.13090⟩ (2021)
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Toward Cumulative Cognitive Science: A Comparison of Meta-Analysis, Mega-Analysis, and Hybrid Approaches
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In: EISSN: 2470-2986 ; Open Mind ; https://hal.archives-ouvertes.fr/hal-03498941 ; Open Mind, MIT Press, 2021, 5, pp.154-173. ⟨10.1162/opmi_a_00048⟩ (2021)
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Socioeconomic status correlates with measures of Language Environment Analysis (LENA) system: a meta-analysis
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In: ISSN: 0305-0009 ; EISSN: 1469-7602 ; Journal of Child Language ; https://hal.archives-ouvertes.fr/hal-03498959 ; Journal of Child Language, Cambridge University Press (CUP), In press, pp.1-15. ⟨10.1017/S0305000921000441⟩ (2021)
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The effect of Crianza Positiva e-messaging program on adult-child language interactions
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In: Behavioral Public Policy ; https://hal.archives-ouvertes.fr/hal-03498848 ; Behavioral Public Policy, 2021 (2021)
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Describing Vocalizations in Young Children: A Big Data Approach Through Citizen Science Annotation
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In: ISSN: 1092-4388 ; EISSN: 1558-9102 ; Journal of Speech, Language, and Hearing Research ; https://hal.archives-ouvertes.fr/hal-03498946 ; Journal of Speech, Language, and Hearing Research, American Speech-Language-Hearing Association, 2021, 64 (7), pp.2401-2416. ⟨10.1044/2021_JSLHR-20-00661⟩ (2021)
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Abstract:
International audience ; Purpose Recording young children's vocalizations through wearables is a promising method to assess language development. However, accurately and rapidly annotating these files remains challenging. Online crowdsourcing with the collaboration of citizen scientists could be a feasible solution. In this article, we assess the extent to which citizen scientists' annotations align with those gathered in the lab for recordings collected from young children. Method Segments identified by Language ENvironment Analysis as produced by the key child were extracted from one daylong recording for each of 20 participants: 10 low-risk control children and 10 children diagnosed with Angelman syndrome, a neurogenetic syndrome characterized by severe language impairments. Speech samples were annotated by trained annotators in the laboratory as well as by citizen scientists on Zooniverse. All annotators assigned one of five labels to each sample: Canonical, Noncanonical, Crying, Laughing, and Junk. This allowed the derivation of two child-level vocalization metrics: the Linguistic Proportion and the Canonical Proportion. Results At the segment level, Zooniverse classifications had moderate precision and recall. More importantly, the Linguistic Proportion and the Canonical Proportion derived from Zooniverse annotations were highly correlated with those derived from laboratory annotations. Conclusions Annotations obtained through a citizen science platform can help us overcome challenges posed by the process of annotating daylong speech recordings. Particularly when used in composites or derived metrics, such annotations can be used to investigate early markers of language delays.
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Keyword:
[SCCO.LING]Cognitive science/Linguistics; [SCCO.PSYC]Cognitive science/Psychology
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URL: https://hal.archives-ouvertes.fr/hal-03498946/document
https://hal.archives-ouvertes.fr/hal-03498946/file/2021_Semenzin_JSLHR.pdf
https://hal.archives-ouvertes.fr/hal-03498946
https://doi.org/10.1044/2021_JSLHR-20-00661
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Non-word repetition in bilingual children: the role of language exposure, vocabulary scores and environmental factors ...
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La ripetizione di non-parole in bambini bilingue, in funzione del loro vocabolario in italiano ...
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First DIHARD Challenge -- System Submissions and Scores ...
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First DIHARD Challenge -- System Submissions and Scores ...
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Learning and Processing Language from Wearables: Opportunities and Challenges ...
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Quantifying Sources of Variability in Infancy Research Using the Infant-Directed-Speech Preference
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A thorough evaluation of the Language Environment Analysis (LENA) system
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In: Behav Res Methods (2021)
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