Deriving Benchmarking Datasets from Long-Form Recordings: Challenges and Opportunities

2026-07-03

AI Summary

This study addresses the limited utility of long-duration child speech recordings in early language development research, hindered by poor cross-corpus compatibility, absence of standardized evaluation protocols, and privacy concerns. To overcome these challenges, the authors propose the first integrated framework that unifies data standardization, reproducible benchmarking, and ethical governance. The framework consolidates 27 open-source child speech datasets, establishes four standardized speech processing benchmark pipelines, and implements a role-based ELSI (Ethical, Legal, and Social Implications) governance system. Validation through a vocal-type classification task demonstrates that the framework substantially enhances model generalizability across languages and recording conditions while ensuring regulatory compliance, thereby achieving a coherent integration of technical rigor and ethical standards.

Abstract

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.

Problem

• long-form recordings
• benchmarking datasets
• child-centered audio
• privacy constraints
• cross-corpus generalization

Innovation

• long-form recordings
• standardized benchmarking
• ethical ML workflow
• child-centered datasets
• speech processing