Managing, storing, and sharing long-form recordings and their annotations

Gautheron, Lucas; Rochat, Nicolas; Cristià, Alejandrina

doi:10.1007/s10579-022-09579-3

Cited by 3 publications

(2 citation statements)

References 54 publications

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“…The collected audio files as well as all the different analysis outputs are assembled into a dedicated dataset. So as to build on current routines in our team, we build on standards and a server that we have described elsewhere [10]. In a nutshell, the dataset is organized using a specific set of standards, which allows us to employ functions that we have used in other work.…”

Section: Managing the Datasetmentioning

confidence: 99%

Full Description of An Automated Pipeline for Providing Personalized Feedback Based on Audio Samples

Peurey,

Havard,

Virlet

et al. 2024

Preprint

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Personalized feedback based on the automated analysis of au- dio samples could be useful in a wide range of intervention contexts, from early childhood to neurodegenerative programs, which target behaviors having vocal correlates. In this paper, we describe an automated pipeline that allows one to provide personalized feedback based on the automated analysis of au- dio samples of caregiver-child conversations captured using a smartphone. The pipeline relies on open-source packages and AWS in order to provide a cheap, reproducible, and consider- ably scalable solution for researchers and practitioners inter- ested in early childhood development and caregiver-child in- teraction, and which could be adapted for other use cases. It processes conversation files that are 1-10 minutes long, with a cost of 0.20 US$ per hour of audio analyzed. It is currently op- erational in one large-scale experiment in Uruguay, where audio files are collected through a chatbot, whose implementation is not covered in this paper. Finally, we lay out limitations of our approach and potential improvements.

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Section: Managing the Datasetmentioning

confidence: 99%

Full Description of An Automated Pipeline for Providing Personalized Feedback Based on Audio Samples

Peurey,

Havard,

Virlet

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

“…Corpus organization, processing, and preliminary analyses were done with ChildProject (Gautheron, Rochat, & Cristia, 2022). Additionally, transparency was ensured by publicly posting all of our materials (https://osf.io/t8r5j/?view_only=4e6f8a3b37f84da681b414bc058deca4, Anonymized, 2022), including code to reproduce results thanks to RMarkdown (Baumer & Udwin, 2015) on R (R Consortium Team, 2013), as well as DataLad (Wagner et al, 2020) and GIN (https://gin.g-node.org/).…”

Section: Analysesmentioning

confidence: 99%

Vocal input and output among infants in a multilingual context: Evidence from long‐form recordings in Vanuatu

Cristià

Gautheron

Colleran

2023

Developmental Science

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The use of Language ENvironment Analysis in autism research: A systematic review

Putnam,

Markfeld,

Wright

et al. 2024

Autism

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The Language ENvironment Analysis (LENA) system has been used increasingly in research to record and evaluate the everyday speech of autistic children and their families. However, it is unclear how researchers are using LENA and whether the system is well-suited for work with autistic individuals. The purpose of this systematic review is to summarize the use of LENA in autism research, to highlight the strengths and limitations of the system as identified by researchers, and to provide recommendations for future research and clinical use. Forty-two studies that used LENA with samples of autistic children were identified through a systematic database search. Researchers using LENA in autism did so across a variety of ages, settings, and analytical approaches. Most studies used LENA within recommended guidelines. The most common purpose of using LENA was for exploratory research. Noted strengths of the LENA system included ecological validity, cost-effectiveness, and timely clinical feedback. Limitations included lower rates of speaker identification compared to human coders and limited information regarding speech context and language development. This systematic review provides key insights into the methods surrounding LENA use in autism research and serves to inform researchers and clinicians on best practices for future use with this technology. Lay abstract In research, language ability has historically been measured using structured tasks in laboratory settings. In recent years, there has been a growing emphasis on the need to instead capture language ability in an individual’s natural setting (i.e. through social interaction or in their home). Considering natural language may be particularly important for the autistic population, as an autistic child’s language ability can be very different depending on the setting. One common tool for capturing natural language is the LENA recording system, which takes audio recordings over long periods of time and provides estimates of children’s and caregivers’ speech. The purpose of this systematic review is to summarize the use of LENA in autism research, to highlight the strengths and limitations of the system as identified by researchers, and to provide recommendations for future research and clinical use. We identified 42 autism studies that used LENA in a variety of ways and settings. Most studies used LENA within the guidelines put forth by its creators, and it was most commonly used to understand speech or speech development for autistic children. LENA is a useful tool for clinicians and caregivers to gain some insights into child speech, but those considering using it should be aware of concerns about its accuracy and limitations about the information it provides. In this review, we supplement the official LENA guidelines with specific suggestions for use with the autistic population.

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Managing, storing, and sharing long-form recordings and their annotations

Cited by 3 publications

References 54 publications

Full Description of An Automated Pipeline for Providing Personalized Feedback Based on Audio Samples

Full Description of An Automated Pipeline for Providing Personalized Feedback Based on Audio Samples

Vocal input and output among infants in a multilingual context: Evidence from long‐form recordings in Vanuatu

The use of Language ENvironment Analysis in autism research: A systematic review

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