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arXiv · 2602.01634

HuPER: A Human-Inspired Framework for Phonetic Perception

Chenxu Guo·Jiachen Lian·Yisi Liu·Baihe Huang·Shriyaa Narayanan·Cheol Jun Cho·Gopala Anumanchipalli
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Abstract

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.

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Chenxu Guo, Jiachen Lian, Yisi Liu, Baihe Huang, Shriyaa Narayanan, Cheol Jun Cho, Gopala Anumanchipalli. 2026-02-02. HuPER: A Human-Inspired Framework for Phonetic Perception. https://arxiv.org/abs/2602.01634

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