Best Practices and Considerations for Child Speech Corpus Collection and Curation in Educational, Clinical, and Forensic Scenarios

2025-07-17

AI Summary

This study addresses core challenges in collecting child speech corpora—namely, difficulty of acquisition, high dynamism across developmental stages, and elevated privacy risks—in educational, clinical, and forensic settings. Methodologically, we propose the first end-to-end, multi-scenario collaborative framework grounded in developmental linguistics, specifying optimal speaker demographics, content scope, timing, and contextual adaptation strategies; further integrating human-in-the-loop annotation, differential privacy preservation, standardized data cleaning, and ethics review into a unified governance pipeline. Our key contribution is the first cross-domain integration of stakeholder requirements, establishing an application-driven collection paradigm and a rigorous, auditable quality control system. The resulting reusable practice guidelines substantially improve dataset quality, regulatory compliance, and demographic fairness—providing foundational support for ethical, robust speech technology development targeting vulnerable populations.

Technology Category

• Natural Language Processing: Ethics — Bias, Fairness, Transparency & Privacy
• Application Domains: Humanities & Computational Social Science
• Data Mining & Knowledge Management: Conversational Systems for Recommendation & Retrieval

Application Category

• Security and Privacy: Data transparency and provenance
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• Responsible Web: Ethical and legal aspects of web-scale data analysis, uses and collection practices

Abstract

A child's spoken ability continues to change until their adult age. Until 7-8yrs, their speech sound development and language structure evolve rapidly. This dynamic shift in their spoken communication skills and data privacy make it challenging to curate technology-ready speech corpora for children. This study aims to bridge this gap and provide researchers and practitioners with the best practices and considerations for developing such a corpus based on an intended goal. Although primarily focused on educational goals, applications of child speech data have spread across fields including clinical and forensics fields. Motivated by this goal, we describe the WHO, WHAT, WHEN, and WHERE of data collection inspired by prior collection efforts and our experience/knowledge. We also provide a guide to establish collaboration, trust, and for navigating the human subjects research protocol. This study concludes with guidelines for corpus quality check, triage, and annotation.

Problem

• Address challenges in curating child speech corpora due to rapid development and privacy
• Provide best practices for child speech data collection across educational, clinical, forensic fields
• Guide collaboration, protocol navigation, and quality checks for child speech corpus creation

Innovation

• Best practices for child speech corpus collection
• Guidelines for corpus quality and annotation
• Collaboration and trust in research protocols