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| Autores principales: | , , , , , , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2026
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| Materias: | |
| Acceso en línea: | https://arxiv.org/abs/2605.29257 |
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| _version_ | 1866916058358611968 |
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| author | Feng, Tiantian Xu, Anfeng Shi, Xuan Kommineni, Aditya Siam, Shakhrul Iman Micheletti, Megan Shi, Zhonghao Tager-Flusberg, Helen Zhang, Mi Perry, Lynn K. Lord, Catherine Messinger, Daniel Narayanan, Shrikanth |
| author_facet | Feng, Tiantian Xu, Anfeng Shi, Xuan Kommineni, Aditya Siam, Shakhrul Iman Micheletti, Megan Shi, Zhonghao Tager-Flusberg, Helen Zhang, Mi Perry, Lynn K. Lord, Catherine Messinger, Daniel Narayanan, Shrikanth |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_29257 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | ChildVox: A Speech, Audio, and Large Audio-Language Model Benchmark in Understanding and Characterizing Sound across Childhood Feng, Tiantian Xu, Anfeng Shi, Xuan Kommineni, Aditya Siam, Shakhrul Iman Micheletti, Megan Shi, Zhonghao Tager-Flusberg, Helen Zhang, Mi Perry, Lynn K. Lord, Catherine Messinger, Daniel Narayanan, Shrikanth Sound 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. |
| title | ChildVox: A Speech, Audio, and Large Audio-Language Model Benchmark in Understanding and Characterizing Sound across Childhood |
| topic | Sound |
| url | https://arxiv.org/abs/2605.29257 |