Governed Self-Correction for Low-Resource N'Ko ASR: A Technical Report on the Acoustic Verifier Experiment

# Governed Self-Correction for Low-Resource N'Ko ASR: A Technical Report on the Acoustic Verifier Experiment **Date:** 2026-06-01 **Author:** Mohamed Diomande **Status:** Component-characterized; loop not yet closed. Preservation/data-selection signal confirmed (clean preservation AUC 0.739; original 297k/ANE pilot AUC 0.923 was inflated); live acoustic correction is capped (absolute proposal plausibility AUC 0.60); proposal quality identified as the main bottleneck. **Scope:** This report documents the full experimental chain from the AGP correction benchmark through the acoustic verifier, including every measured number, every dead end, and the precise wiring required to reproduce it. The N'Ko speech program set out to build a **self-improving correction loop**: an ASR model decodes audio to toneless N'Ko, a language model proposes corrections, a governance gate accepts only admissible corrections, and the accepted/rejected pairs are recycled as training data. This report covers the experimental campaign that tested whether that loop closes. 1. **An ungoverned LLM corrector is catastrophic to a low-resource transcript.** Blind acceptance of a Gemma-3n correction model moved CER from 0.3106 to 0.4701 (**+15.94pp worse**) on a 500-row real-proposal benchmark. The governance gate neutralized this to +0.14pp (a 99% reduction of harm). Governance *preserves*. 2. **No text-internal signal can build a correctness gate.** Across trajectory scalars, n-best consensus, and character posteriors, the area-under-curve for predicting whether a proposed edit actually lowers CER was ~0.50 (chance). Good edits and bad edits are statistically indistinguishable from the text side. This was proven conclusively, not assumed.