@inproceedings{feng-etal-2026-childvox,
title = "{C}hild{V}ox: A Speech, Audio, and Large Audio-Language Model Benchmark in Understanding and Characterizing Sound across Childhood",
author = "Feng, Tiantian and
Xu, Anfeng and
Shi, Xuan and
Kommineni, Aditya and
Siam, Shakhrul Iman and
Micheletti, Megan and
Shi, Zhonghao and
Tager-Flusberg, Helen and
Zhang, Mi and
Perry, Lynn K and
Lord, Catherine and
Messinger, Daniel and
Narayanan, Shrikanth",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.emnlp-main.1756/",
pages = "40054--40072",
ISBN = "979-8-89176-459-0",
abstract = "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. Our benchmark models on public datasets are released at: https://github.com/tiantiaf0627/childvox-release."
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<abstract>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. Our benchmark models on public datasets are released at: https://github.com/tiantiaf0627/childvox-release.</abstract>
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%0 Conference Proceedings
%T ChildVox: A Speech, Audio, and Large Audio-Language Model Benchmark in Understanding and Characterizing Sound across Childhood
%A Feng, Tiantian
%A Xu, Anfeng
%A Shi, Xuan
%A Kommineni, Aditya
%A Siam, Shakhrul Iman
%A Micheletti, Megan
%A Shi, Zhonghao
%A Tager-Flusberg, Helen
%A Zhang, Mi
%A Perry, Lynn K.
%A Lord, Catherine
%A Messinger, Daniel
%A Narayanan, Shrikanth
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F feng-etal-2026-childvox
%X 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. Our benchmark models on public datasets are released at: https://github.com/tiantiaf0627/childvox-release.
%U https://aclanthology.org/2026.emnlp-main.1756/
%P 40054-40072
Markdown (Informal)
[ChildVox: A Speech, Audio, and Large Audio-Language Model Benchmark in Understanding and Characterizing Sound across Childhood](https://aclanthology.org/2026.emnlp-main.1756/) (Feng et al., EMNLP 2026)
ACL
- 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, and Shrikanth Narayanan. 2026. ChildVox: A Speech, Audio, and Large Audio-Language Model Benchmark in Understanding and Characterizing Sound across Childhood. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 40054–40072, Budapest, Hungary. Association for Computational Linguistics.