8 papers
Deriving Benchmarking Datasets from Long-Form Recordings: Challenges and Opportunities
Kaveri K. Sheth, Lawrence Borst, Tarek Kunze +6
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 a…
BabyHuBERT: Multilingual Self-Supervised Learning for Segmenting Speakers in Child-Centered Long-Form Recordings
Théo Charlot, Tarek Kunze, Maxime Poli +3
Child-centered daylong recordings are essential for studying early language development, but existing speech models trained on clean adult data perform poorly due to acoustic and l…
Context-aware child-directed speech detection from long-form recordings
Théo Charlot, Tarek Kunze, Kaveri K. Sheth +2
Automatically distinguishing child-directed speech from adult-directed speech in long-form recordings is key to scalable analyses of children's language environments. Existing appr…
Classification errors distort findings in automated speech processing: examples and solutions from child-development research
Lucas Gautheron, Evan Kidd, Anton Malko +2
With the advent of wearable recorders, scientists are increasingly turning to automated methods of analysis of audio and video data in order to measure children's experience, behav…
Automated Analysis of Naturalistic Recordings in Early Childhood: Applications, Challenges, and Opportunities
Jialu Li, Marvin Lavechin, Xulin Fan +4
Naturalistic recordings capture audio in real-world environments where participants behave naturally without interference from researchers or experimental protocols. Naturalistic l…
Employing self-supervised learning models for cross-linguistic child speech maturity classification
Theo Zhang, Madurya Suresh, Anne S. Warlaumont +3
Speech technology systems struggle with many downstream tasks for child speech due to small training corpora and the difficulties that child speech pose. We apply a novel dataset,…