HuPER：一种受人类启发的语音感知框架
HuPER: A Human-Inspired Framework for Phonetic Perception
AI总结：
HuPER是一种受人类启发的语音感知框架，通过自适应推断实现高效语音识别，仅需100小时训练数据即可在多个语言基准中取得最佳性能，并支持跨语言零样本迁移。
摘要：
我们提出HuPER，一种受人类启发的框架，将语音感知建模为对语音-声学证据和语言知识的自适应推断。仅使用100小时的训练数据，HuPER在五个英语基准上实现了最先进的语音错误率，并在95种未见过的语言上实现了强大的零样本迁移。HuPER也是第一个能够在多路径语音感知下适应不同声音条件的框架。所有训练数据、模型和代码均已开源。代码和演示可访问https://github.com/HuPER29/HuPER。
英文摘要：
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/Berkeley-Speech-Group/HuPER.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
