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ADR-v2-069 — Hard Contextual Biasing (hotword trie)

Status: Accepted (2026-07-02) · Wave I Context links: [[adr-v2-039-context-primed-dictation]] (soft prompt), [[adr-v2-041-personal-speech-adapter]] (LoRA training), [[adr-011]]

Context

Wave I research (#1) — make names/jargon/personal vocab actually get recognized, not just softly hinted. initial_prompt priming is soft and unreliable for OOV words; LoRA needs training. A prefix-trie built from the user's vocabulary can bias decoding (logit boosting) or rescore the N-best so rare terms win — retraining-free. Anchors: WCTC-Biasing (arXiv 2506.01263), trie K-step biasing (2509.09196), sherpa-onnx hotwords, Whisper zero-shot rare-word biasing (2502.11572).

Distinct from Context-Primed (soft prompt) and Personal Adapter (LoRA fine-tune).

Decision

Add an opt-in Hard Contextual Biasing: [hotwords] enabled=false, boost=2.0. Pure cores: HotwordTrie (char-level insert / is_term / has_prefix), build_hotword_trie(terms), and rescore_nbest(hypotheses, trie, boost) — a post-hoc N-best rescorer that adds boost per hotword hit and re-ranks, needing no model internals. The deeper CTC/attention logit-biasing hook (sherpa-onnx / WFST) is deferred behind a biasing extra. OFF by default.

Consequences

  • Retraining-free rare-word recognition; the pure rescorer already helps, the deferred hook adds in-decoder biasing.
  • Distinct from soft-prompt priming and LoRA.
  • Privacy (ADR-011): trie built locally from the user's own words; nothing transmitted.
  • Caveat: the pure rescorer is token-level (single-word terms); multi-word phrase biasing is the deferred decoder tier. Off by default.