[Submitted on 11 Nov 2026]
Authors:Aoxiong Yin, Auden Li, Chao Huang, Chuanyang Hu, Eitan Wang, Ethan Zou, Fangming Feng, Guangyu Chen, Guinan Li, He Wang, Jiahong Li, Meng Yu, Mingyu Cui, Peng Wang, Rongjie Li, Steve Yves, Teddy Sun, Tian Zheng, Wei Liu, Yang Zhou, Yixuan Zhang, Zhennan Lin, Zhihang Xu, Zhuohao Li, Ziqiang Zhang, Anji Zhang, Bang Wang, Caisheng Ouyang, Chengqi Zhu, Chong Li, Conglian Xie, Dongdong Wang, Edward Wang, Evelynn Liu, Ferman Yang, Feng Lu, Gang Zhang, Jathon Zhang, Jaxl Liu, Jiachen Luo, Jian Li, Jingxi Gui, Kysun Wang, Liang Dong, Longsha Zhou, Na Hu, Qi Chen, Rilin Chen, Rui Yuan, Shanshan Sang, Siyu Yan, Steven Hong, Taihui Wang, Weiwei Li, Xiangyu Zhang, Xiaotong Yang, Xue Hu, Xue Jiang, Yameng Li, Yifu Sun, Yuhuan Zhou, Zhiang Chen, Zhibin Wang
Abstract:We present Hy ASR 3.0, a large language model (LLM)-based automatic speech recognition (ASR) system that delivers strong recognition accuracy, supports flexible contextual conditioning, and maintains robust recognition performance across diverse speech conditions. Hy ASR 3.0 supports Mandarin Chinese, English and 13 Chinese dialect groups, including Cantonese. Among the evaluated systems, it achieves the lowest aggregate error rates on public Mandarin Chinese, English, and Cantonese benchmarks and the lowest error rates on the corresponding internal test sets, alongside first-tier dialect recognition performance. Beyond audio-only transcription, Hy ASR 3.0 supports multiple forms of external context, including hotword lists, question–answer dialogues, and summaries, enabling the use of task-relevant information during recognition. The model also leverages intra-utterance context to disambiguate homophonic and phonetically similar keywords using information from other parts of the same utterance, without requiring a separate contextual input. Evaluations demonstrate strong contextual recognition in both external-context and intra-utterance settings, as well as robust recognition of long-form speech, children’s and older adults’ speech, low-volume speech, and speech in noisy environments. This report describes the model architecture and training methodology and presents evaluations across recognition accuracy, context utilization, and robustness.
Submission history
From: Eitan Wang [view email]
[v1] Wed, 11 Nov 2026 02:01:29 UTC (336 KB)
