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

Hugging Face Daily Papers Papers

Summary

ChildVox presents a comprehensive benchmark for analyzing children's acoustic communication across developmental stages, integrating over 20 sub-tasks from 17 child-centered audio and speech datasets.

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.
Original Article
View Cached Full Text

Cached at: 05/29/26, 07:00 AM

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

Source: https://huggingface.co/papers/2605.29257 Authors:

,

,

,

,

,

,

,

,

,

,

,

Abstract

ChildVox presents a comprehensive benchmark for analyzing children’s acoustic communication across developmental stages using diverse audio and speech models.

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 ofaudio and speech foundation models, including self-supervised, ASR-oriented, andlarge audio-language models, on tasks includingphysiological sound classification, vocalization andcanonical syllables modeling, andspeech quality assessmentand 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.

View arXiv pageView PDFProject pageAdd to collection

Models citing this paper0

No model linking this paper

Cite arxiv.org/abs/2605.29257 in a model README.md to link it from this page.

Datasets citing this paper0

No dataset linking this paper

Cite arxiv.org/abs/2605.29257 in a dataset README.md to link it from this page.

Spaces citing this paper0

No Space linking this paper

Cite arxiv.org/abs/2605.29257 in a Space README.md to link it from this page.

Collections including this paper1

Similar Articles

VoxMem: Benchmarking Multimodal Memory in Large Audio Language Models

Hugging Face Daily Papers

VoxMem is a benchmark for evaluating multimodal memory in Large Audio Language Models, focusing on acoustic evidence types and multi-session memory operations. It reveals significant gaps in current models, such as poor retention of speaker identity and paralinguistic cues compared to semantic content.

RedVox: Safety and Fairness Gaps in Speech Models Across Languages

arXiv cs.CL

This paper introduces RedVox, a multilingual safety and fairness benchmark for speech models. Evaluating eight state-of-the-art models across five languages, it finds persistent vulnerabilities that worsen in non-English settings and with spoken input.

SpeechDx: A Multi-Task Benchmark for Clinical Speech AI

arXiv cs.AI

SpeechDx is a large-scale benchmark for clinical speech AI spanning 12 datasets and 27 tasks across diverse health conditions, structured by stages of speech production. It evaluates 12 state-of-the-art audio encoders and shows that current models do not generalize reliably across the clinical speech landscape.