Boundary-Free Contextual Biasing: Depth-Adaptive Gating and Reading-Space Matching for Unsegmented Languages
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Electrical Engineering and Systems Science > Audio and Speech Processing
arXiv:2610.09467 (eess)
[Submitted on 7 Oct 2026]
Title:Boundary-Free Contextual Biasing: Depth-Adaptive Gating and Reading-Space Matching for Unsegmented Languages
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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)
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View a PDF of the paper titled Boundary-Free Contextual Biasing: Depth-Adaptive Gating and Reading-Space Matching for Unsegmented Languages, by Muhammad Huzaifah and 4 other authors
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