Deriving Benchmarking Datasets from Long-Form Recordings: Challenges and Opportunities
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Abstract (arXiv)
Long-form recordings (LFRs) of child-centered audio are ecologically valid sources for studying early language development, but three problems limit their use. First, LFR corpora are collected across sites with heterogeneous formats and consent structures, making cross-corpus use non-trivial. Second, without standardized benchmarks, assessing whether tools generalize across languages and conditions is hard. Third, ML workflows rarely respect privacy constraints governing sensitive child speech. This paper presents a framework addressing all three: a standardized collection of 27 child-centered datasets built with open-source tools (S1); a replicable pipeline for four speech-processing benchmarks (S2); and ELSI, a role-based ecosystem embedding ethical governance into the ML workflow (S3). We demonstrate the framework via a voice type classification case study and show the three solutions are mutually dependent.
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References · 4
- 2021Systematic Inequalities in Language Technology Performance across the World's Languagescited by 120
- 2025BabyHuBERT: Multilingual Self-Supervised Learning for Segmenting Speakers in Child-Centered Long-Form Recordingscited by 5
- 2025Fifteen Years of Child-Centered Long-Form Recordings: Promises, Resources, and Remaining Challenges to Validitycited by 4
- 2025Employing self-supervised learning models for cross-linguistic child speech maturity classificationcited by 2
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