标题： BabyHuBERT：多语言自监督学习用于以儿童为中心的长格式录音中的说话人分割 显示英文标题

标题： BabyHuBERT: Multilingual Self-Supervised Learning for Segmenting Speakers in Child-Centered Long-Form Recordings

摘要： 以儿童为中心的长时录音对于研究早期语言发展至关重要，但现有的在干净的成人数据上训练的语音模型由于声学和语言差异表现不佳。 我们引入了BabyHuBERT，这是第一个在跨越40种语言的13000小时多语言以儿童为中心的长时录音上训练的自监督语音表示模型。 我们在说话人分割任务上评估了BabyHuBERT，识别目标儿童说话的时间与女性成人、男性成人或其他儿童的区别——这是分析自然语言体验的基本预处理步骤。 BabyHuBERT在六个不同的数据集上取得了52.1%到74.4%的F1分数，始终优于W2V2-LL4300（在英语长时录音上训练）和标准HuBERT（在干净的成人语音上训练）。 显著的改进包括在瓦努阿图和所罗门群岛语料库上比HuBERT分别高出13.2和15.9的绝对F1分数，这表明在未充分代表的语言中也有效。 通过分享代码和模型，BabyHuBERT为儿童语音研究提供了一个基础模型，使得在各种下游任务上进行微调成为可能。 显示英文摘要

摘要： Child-centered long-form 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, the first self-supervised speech representation model trained on 13,000 hours of multilingual child-centered long-form recordings spanning over 40 languages. We evaluate BabyHuBERT on speaker segmentation, identifying when target children speak versus female adults, male adults, or other children -- a fundamental preprocessing step for analyzing naturalistic language experiences. BabyHuBERT achieves F1-scores from 52.1% to 74.4% across six diverse datasets, consistently outperforming W2V2-LL4300 (trained on English long-forms) and standard HuBERT (trained on clean adult speech). Notable improvements include 13.2 absolute F1 points over HuBERT on Vanuatu and 15.9 points on Solomon Islands corpora, demonstrating effectiveness on underrepresented languages. By sharing code and models, BabyHuBERT serves as a foundation model for child speech research, enabling fine-tuning on diverse downstream tasks.