Speech Calibration and Acoustic Improvement v0

The Speech Inscription Bridge v0 changed the failure mode. The harness no longer treats unstable CTC output as language. The next stage is calibration: collect short Malinke recordings, attach expected labels, sort the evidence by failure type, and build evaluation or training candidates without poisoning the corpus. The core invariant is still evidence first. A live packet is not a transcript. A user-supplied expected phrase is not automatically ground truth. A model output is never a label. The calibration compiler only admits a packet for acoustic training when the packet validates and the label is explicitly marked `human_verified`. Phrases supplied in the app text field or through `--nko-headless-expected-phrase` are `operator_expected`: they are useful for evaluation and triage, but they are not training labels until verified. It scans one or more copied `NKOLiveCalibration` roots, validates each `manifest.json` with the Speech Inscription validator, joins optional JSONL labels by `packetId`, `manifestSha256`, or `archiveRef`, and writes a replayable calibration index. The default input is the latest live proof copy: Buckets are derived from typed transcript decisions, not from visual inspection: Each calibration example preserves the manifest path, packet directory, manifest hash, source kind, creation time, validation result, audio evidence records, replay requirements, transcript-decision statistics, expected-label claim, label comparison when meaningful, FAC target placeholders, tone-fusion readiness, and admissibility decision.