collaborators

5 papers

eess.AS2026

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…

eess.AS2026

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…

eess.AS2025

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…

eess.AS2025

Fifteen Years of Child-Centered Long-Form Recordings: Promises, Resources, and Remaining Challenges to Validity

Loann Peurey, Marvin Lavechin, Tarek Kunze +4

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…

eess.AS2025

Challenges in Automated Processing of Speech from Child Wearables: The Case of Voice Type Classifier

Tarek Kunze, Marianne Métais, Hadrien Titeux +5

Recordings gathered with child-worn devices promised to revolutionize both fundamental and applied speech sciences by allowing the effortless capture of children's naturalistic spe…