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BabyHuBERT: Multilingual Self-Supervised Learning for Seg...
[Submitted on 18 Sep 2025 (v1), last revised 29 Jun 2026 (this v · 2025-09-18 · via eess.AS updates on arXiv.org

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Abstract: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 linguistic differences. We introduce BabyHuBERT, a self-supervised speech model trained on 13,000 hours of multilingual child-centered recordings from 40+ languages. Evaluated on voice type classification, the task of identifying who produces speech and when in child-centered recordings (key child, other children, male, and female adults), BabyHuBERT-VTC achieves F1-scores from 55.0% to 76.1% across six corpora, consistently outperforming W2V2-LL4300 and HuBERT (pretrained on English daylongs and clean adult speech, respectively). Notable gains include 14.0 and 18.3 absolute F1 points over HuBERT on Vanuatu and Solomon Islands, demonstrating effectiveness on underrepresented languages. We share code and models to support researchers working with child-centered recordings across diverse linguistic contexts.

Submission history

From: Théo Charlot [view email]
[v1] Thu, 18 Sep 2025 14:34:17 UTC (57 KB)
[v2] Thu, 5 Mar 2026 16:26:56 UTC (52 KB)
[v3] Mon, 29 Jun 2026 13:06:46 UTC (52 KB)