Daily auditory environments in French-speaking infants: A longitudinal dataset

Estelle Hervé, Clément François, Laurent Prevot

Abstract
Babies’ daily auditory environment plays a crucial role in language development. Most previous research estimating the quantitative and qualitative aspects of early speech inputs has predominantly focused on English- and Spanish-speaking families. In addition, validation studies for daylong recordings’ analysis tools are scarce on French data sets.In this paper, we present a French corpus of daylong audio recordings longitudinally collected with the LENA (Language ENvironment Analysis) system from infants aged 3 to 24 months. We conduct a thorough exploration of this data set, which serves as a quality check for both the data and the analysis tools.We evaluate the reliability of LENA metrics by systematically comparing them with those obtained from the ChildProject set of tools and by checking the known dynamics of the metrics with age. These metrics are also used to replicate, on our data set, findings from (Warlaumont et al, 2014) about the increase of infants’ speech vocalizations and temporal contingencies between infants and caregivers with age.
Anthology ID:
2024.cmcl-1.12
Volume:
Proceedings of the Workshop on Cognitive Modeling and Computational Linguistics
Month:
August
Year:
2024
Address:
Bangkok, Thailand
Editors:
Tatsuki Kuribayashi, Giulia Rambelli, Ece Takmaz, Philipp Wicke, Yohei Oseki
Venues:
CMCL | WS
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
132–151
Language:
URL:
https://aclanthology.org/2024.cmcl-1.12
DOI:
Bibkey:
Cite (ACL):
Estelle Hervé, Clément François, and Laurent Prevot. 2024. Daily auditory environments in French-speaking infants: A longitudinal dataset. In Proceedings of the Workshop on Cognitive Modeling and Computational Linguistics, pages 132–151, Bangkok, Thailand. Association for Computational Linguistics.
Cite (Informal):
Daily auditory environments in French-speaking infants: A longitudinal dataset (Hervé et al., CMCL-WS 2024)
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