ChildVox: A Speech, Audio, and Large Audio-Language Model Benchmark in Understanding and Characterizing Sound across Childhood

Tiantian Feng, Anfeng Xu, Xuan Shi, Aditya Kommineni, Shakhrul Iman Siam, Megan Micheletti, Zhonghao Shi, Helen Tager-Flusberg, Mi Zhang, Lynn K. Perry, Catherine Lord, Daniel Messinger, Shrikanth Narayanan

ChildVox: A Speech, Audio, and Large Audio-Language Model Benchmark in Understanding and Characterizing Sound across Childhood: 5 upvotes on Hugging Face Daily Papers, #37 of 59 papers on 2026-05-29. Day-by-day upvote history. It lost 7 votes when the Hub removed votes in bulk.

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.

Paper page on Hugging Face · arXiv

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