arXiv：cs.CL· Muhammad Huzaifah, Yu Pan, Zachary Yeo, Ningjie Bai, Guangzhao Yang·· 3 小时前AI 评分38
面向非分词语言的无边界上下文偏置：深度自适应门控与读法空间匹配
Boundary-Free Contextual Biasing: Depth-Adaptive Gating and Reading-Space Matching for Unsegmented Languages
AI 导读
研究者提出一种无边界偏置解码器，基于字符级 Aho-Corasick 自动机，无需训练和二次解码，即可为冻结的公开 CTC 模型提供上下文偏置。在 Aishell-1 NE 硬 R1 子集上召回率达 66.5%，超过经过训练的 CLAS 基线（64%），并可迁移至 WenetSpeech 及另一种架构而无需重新调参。
正文
Abstract:Contextual biasing supplies an ASR system with a list of expected words at inference time, but existing methods rely on word boundaries that Japanese and Chinese do not provide. We present a boundary-free biasing decoder for frozen public CTC models, built on a character-level Aho-Corasick automaton, with no training and no second pass. Two evidence-based mechanisms replace the boundary: a depth-adaptive gate that sets how hard to push from match depth, and reading-space matching for when the audio is right but the characters are wrong. On Aishell-1 NE's hard R1 subset we reach 66.5% recall, above the trained CLAS baseline (64%), transferring to WenetSpeech and to a second architecture without retuning. We release the first open Japanese contextual-biasing benchmark, where biasing lifts rare-word recall by 25 points at precision above 97%, and still by 19 and 22 points against 1,000-word lists.
| Subjects: | Audio and Speech Processing (eess.AS); Computation and Language (cs.CL); Sound (cs.SD) |
| Cite as: | arXiv:2610.09467 [eess.AS] |
| (or arXiv:2610.09467v1 [eess.AS] for this version) | |
| https://doi.org/10.48550/arXiv.2610.09467 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Muhammad Huzaifah Md Shahrin [view email]
[v1]
Wed, 7 Oct 2026 05:23:18 UTC (46 KB)
来源：arXiv：cs.CL · arxiv.org