Fifteen Years of Child-Centered Long-Form Recordings: Promises, Resources, and Remaining Challenges to Validity
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arXiv
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| Autori principali: | , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| Soggetti: | |
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| _version_ | 1866914016791625728 |
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| author | Peurey, Loann Lavechin, Marvin Kunze, Tarek Khentout, Manel Gautheron, Lucas Dupoux, Emmanuel Cristia, Alejandrina |
| author_facet | Peurey, Loann Lavechin, Marvin Kunze, Tarek Khentout, Manel Gautheron, Lucas Dupoux, Emmanuel Cristia, Alejandrina |
| contents | Audio-recordings collected with a child-worn device are a fundamental tool in child language research. Long-form recordings collected over whole days promise to capture children's input and production with minimal observer bias, and therefore high validity. The sheer volume of resulting data necessitates automated analysis to extract relevant metrics for researchers and clinicians. This paper summarizes collective knowledge on this technique, providing entry points to existing resources. We also highlight various sources of error that threaten the accuracy of automated annotations and the interpretation of resulting metrics. To address this, we propose potential troubleshooting metrics to help users assess data quality. While a fully automated quality control system is not feasible, we outline practical strategies for researchers to improve data collection and contextualize their analyses. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_11075 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Fifteen Years of Child-Centered Long-Form Recordings: Promises, Resources, and Remaining Challenges to Validity Peurey, Loann Lavechin, Marvin Kunze, Tarek Khentout, Manel Gautheron, Lucas Dupoux, Emmanuel Cristia, Alejandrina Audio and Speech Processing Machine Learning Sound Audio-recordings collected with a child-worn device are a fundamental tool in child language research. Long-form recordings collected over whole days promise to capture children's input and production with minimal observer bias, and therefore high validity. The sheer volume of resulting data necessitates automated analysis to extract relevant metrics for researchers and clinicians. This paper summarizes collective knowledge on this technique, providing entry points to existing resources. We also highlight various sources of error that threaten the accuracy of automated annotations and the interpretation of resulting metrics. To address this, we propose potential troubleshooting metrics to help users assess data quality. While a fully automated quality control system is not feasible, we outline practical strategies for researchers to improve data collection and contextualize their analyses. |
| title | Fifteen Years of Child-Centered Long-Form Recordings: Promises, Resources, and Remaining Challenges to Validity |
| topic | Audio and Speech Processing Machine Learning Sound |
| url | https://arxiv.org/abs/2506.11075 |