LexiAdapt: Auditing Contextual Speech Recognition for Unsupported Phrase Insertions

Contextual automatic speech recognition (ASR) uses phrase lists from documents, contacts, or domain vocabularies to recover rare terms. Yet the recognizer can copy a listed phrase that was not spoken. Word error rate (WER) measures errors across all words. Bias-word error rate (B-WER) focuses on listed rare words, while phrase recall measures recovery. None reveals by itself whether the audio supports an accepted edit or whether a safe-looking system rejects almost every edit. We introduce LexiAdapt: it selects contextual edits without the reference and audits the transcript against it afterward. LexiAdapt tracks where each phrase came from. No-leakage lists exclude the target transcript. It also fixes the acceptance rule before evaluation. Its scorecard reports separate error rates for listed rare words and all other words. It also reports phrase precision and recall, their harmonic mean, and unsupported additions. Coverage records how often the system accepts a changed contextual transcript. Overall WER and real-time factor capture transcription quality and cost. The comparison contract covers a primary modern contextual model, a larger streaming contextual model, a Whisper anchor, and a no-context control. The design requires disjoint development and test units, frozen phrase sources and gates, matched contextual interfaces, and uncertainty at the highest independent unit. This makes the central question falsifiable: under matched conditions, does context improve bias-word and phrase recovery without increasing unsupported additions, collapsing coverage, or imposing unacceptable latency? The protocol prevents lower WER or zero observed insertions under near-total abstention from being treated as sufficient evidence.