HuPER: A Human-Inspired Framework for Phonetic Perception
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arXiv
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| Auteurs principaux: | , , , , , , |
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| Format: | Preprint |
| Publié: |
2026
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| _version_ | 1866915768471388160 |
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| author | Guo, Chenxu Lian, Jiachen Liu, Yisi Huang, Baihe Narayanan, Shriyaa Cho, Cheol Jun Anumanchipalli, Gopala |
| author_facet | Guo, Chenxu Lian, Jiachen Liu, Yisi Huang, Baihe Narayanan, Shriyaa Cho, Cheol Jun Anumanchipalli, Gopala |
| contents | We propose HuPER, a human-inspired framework that models phonetic perception as adaptive inference over acoustic-phonetics evidence and linguistic knowledge. With only 100 hours of training data, HuPER achieves state-of-the-art phonetic error rates on five English benchmarks and strong zero-shot transfer to 95 unseen languages. HuPER is also the first framework to enable adaptive, multi-path phonetic perception under diverse acoustic conditions. All training data, models, and code are open-sourced. Code and demo avaliable at https://github.com/HuPER29/HuPER. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_01634 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | HuPER: A Human-Inspired Framework for Phonetic Perception Guo, Chenxu Lian, Jiachen Liu, Yisi Huang, Baihe Narayanan, Shriyaa Cho, Cheol Jun Anumanchipalli, Gopala Audio and Speech Processing Artificial Intelligence We propose HuPER, a human-inspired framework that models phonetic perception as adaptive inference over acoustic-phonetics evidence and linguistic knowledge. With only 100 hours of training data, HuPER achieves state-of-the-art phonetic error rates on five English benchmarks and strong zero-shot transfer to 95 unseen languages. HuPER is also the first framework to enable adaptive, multi-path phonetic perception under diverse acoustic conditions. All training data, models, and code are open-sourced. Code and demo avaliable at https://github.com/HuPER29/HuPER. |
| title | HuPER: A Human-Inspired Framework for Phonetic Perception |
| topic | Audio and Speech Processing Artificial Intelligence |
| url | https://arxiv.org/abs/2602.01634 |