发表机构
Stanford University（斯坦福大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究利用HuBERT-BASE提取的语音嵌入，发现聋/弱听儿童与照料者的嵌入距离随听觉年龄减小，且该距离与言语语言标准化测量相关，为口语发展评估提供了可扩展的语言无关路径。
AI 中文摘要
语言发展的特征是儿童的言语逐渐向成人模式收敛。传统上测量这一过程需要详细的转录和特定语言的专业知识，限制了其在不同语言和人群中的可扩展性。本研究中，我们使用语音嵌入直接从以儿童为中心的日常长录音的声学信号中捕捉这种收敛过程。我们采用HuBERT-BASE，从聋/弱听儿童及其女性成人照料者的言语发声中提取嵌入（观察时长超过925小时）。在控制音高和发声长度的情况下，儿童与照料者之间的嵌入距离随听觉年龄的增长而减小，表明儿童的言语模式在发展过程中确实向照料者收敛。这一单一距离指标还与从婴儿期到学前阶段的多项标准化言语和语言测量结果相关。这些结果为从儿童日常生活中进行可扩展、语言无关的口语语言发展评估提供了一条途径。
英文摘要
Language development is characterized by a gradual convergence of children's speech toward adult patterns. Measuring this process has traditionally required detailed transcription and language-specific expertise, limiting scalability across languages and populations. Here, we use speech embeddings to capture this convergence directly from the acoustic signal in longform, child-centered recordings, taken as children go about their daily lives. Using HuBERT-BASE, we extracted embeddings from speech vocalizations of children who are deaf/hard-of-hearing and their female adult caregivers ($>$925 hrs. observation). Embedding distance between children and caregivers decreased with hearing age, controlling for pitch and vocalization length, indicating, as expected, that children's speech patterns converge to caregivers over development. This single distance metric likewise related to multiple standardized measures of speech and language from infancy through preschoolhood. These results suggest a path toward scalable, language-neutral assessment of spoken language development from children's everyday lives.
Comments10 pages, 5 figures, 2026 ACL CDL Workshop
