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

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 more than 15 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 (e.g., SSAST), ASR-oriented (e.g., Whisper), and large audio-language models (e.g., Qwen2Audio), on tasks ranging from physiological sound classification, through vocalization and canonical-syllable modeling, to 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.