VISH-ASR: Viterbi-Guided Selection of Hotwords for Speech-LLM Contextual ASR
Prompt-based contextual ASR requires selecting a compact hotword subset from large dynamic inventories, yet existing pre-selection methods rely either on discrete ASR hypotheses that may discard acoustic evidence or on separately trained retrieval models. We propose Viterbi-guided Selection of Hotwords (VISH), a training-free, plug-and-play framework that directly verifies candidate-specific acoustic support over frame-level CTC posteriors. VISH formulates pre-selection as constrained posterior search; M-VISH makes large-list verification scalable through frame-synchronous shared-prefix Viterbi search with bounded top-K pruning, while Bi-MVISH exploits complementary backward evidence for ...
| Authors | Xiaoyu Gu, Yu Xi, Chencheng Wang et al. |
|---|---|
| Primary category | eess.AS |
| Published | 20 October 2026 |
| Last updated at source | 20 October 2026 |
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