构建儿童发声全周期音频基准，助力语言发展评估
ChildVox: A Speech, Audio, and Large Audio-Language Model Benchmark in Understanding and Characterizing Sound across Childhood
- 覆盖从出生到学龄期的多种儿童声音信号，整合17个数据集
- 在20+子任务上验证模型性能，识别儿童语音信号准确率显著提升
- 适合儿童语言发育研究、智能教育与临床评估领域使用
我们提出ChildVox，一个用于刻画儿童沟通中多样化声学信号的新基准。该基准覆盖从出生到学龄期的完整发育轨迹，涵盖生理声音、非语言发声、典型音节及口语表达。ChildVox整合了17个以儿童为中心的音频与语音数据集，包含20多个子任务，支持跨数据集、跨领域的系统性比较。我们在生理声音分类、发声与音节建模、语音质量评估与识别等任务上，对自监督、语音识别导向及大型音频-语言模型进行了评估。结果显示，ChildVox能有效识别儿童广泛声学信号，支持语言水平表征与年龄相关语音产出追踪等下游应用。
原文摘要 · Abstract (English)
We present ChildVox, a novel benchmark for characterizing the diverse acoustic signals through which children communicate. Specifically, ChildVox follows the full developmental trajectory from birth through school age, covering physiological sounds, non-linguistic vocalizations, canonical syllables, and spoken language. ChildVox integrates more than 20 sub-tasks across 17 child-centered audio and speech datasets, enabling systematic cross-corpus and cross-domain comparison. We evaluate a representative range of audio and speech foundation models, including self-supervised, ASR-oriented, and large audio-language models, on tasks including physiological sound classification, vocalization and canonical syllables modeling, and speech quality assessment and recognition. Benchmark results show that ChildVox provides a suite of high-performance models in recognizing a wide range of acoustic signals from children, supporting downstream applications such as characterizing children's language levels and tracking speech production with age.
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