[
    {
        "id": "osp-17476",
        "type": "article-journal",
        "title": "Boundary-Free Contextual Biasing: Depth-Adaptive Gating and Reading-Space Matching for Unsegmented Languages",
        "author": [
            {
                "family": "Huzaifah",
                "given": "Muhammad"
            },
            {
                "family": "Pan",
                "given": "Yu"
            },
            {
                "family": "Yeo",
                "given": "Zachary"
            },
            {
                "family": "Bai",
                "given": "Ningjie"
            },
            {
                "family": "Yang",
                "given": "Guangzhao"
            }
        ],
        "URL": "https://omanscience.com/en/articles/boundary-free-contextual-biasing-depth-adaptive-gating-and-reading-space-matching-for-unsegmented-languages",
        "language": "en",
        "issued": {
            "date-parts": [
                [
                    2026
                ]
            ]
        },
        "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."
    }
]