Featural Acoustic Coding with N'Ko — Program Documentation

This document is the dense, self-contained account of a research direction that began as a reaction to a single paper and turned into a structural extension of an existing N'Ko language-technology program. The reaction paper is Lexical Acoustic Coding, abbreviated LAC, which proposes that a short sound can be transmitted between two language-model agents as a readable English sentence and then re-rendered from that sentence, trading exact sample recovery for perceptual similarity. The reaction was that natural English is the weakest part of that design and that a script engineered for sound, specifically N'Ko, would carry the same information more faithfully and far more compactly. That reaction was developed into a method called Featural Acoustic Coding, abbreviated FAC, a written paper that compiles cleanly, a battery of experiments that were actually run rather than asserted, and a strategic reconciliation with the brain-scanner research program that already produced a script-native N'Ko speech recognizer at roughly twenty percent character error rate. The honest conclusion, stated up front so nothing here oversells, is that FAC has real merit but not as a standalone competitor to LAC; its merit is highest as the acoustic tone channel and the generative dual of the recognizer that already exists, and the standalone codec framing should be demoted to a section rather than promoted to a flagship. LAC works by analyzing a waveform into a small set of interpretable acoustic descriptors that span temporal, spectral, harmonic, and psychoacoustic properties, quantizing each descriptor into a word-like label drawn from a shared vocabulary, and composing those labels into an English sentence such as the one about a mid-power punch with a moderate-onset envelope that stays front-loaded and clipped. A receiver parses the sentence back into acoustic intervals and synthesizes the nearest waveform, with a closed-loop refinement step that nudges the synthesis toward the target. LAC situates itself against three neighbors in the audio representation space: captions, which are readable but too loose to resynthesize from; neural codecs such as SoundStream, EnCodec, and the Descript Audio Codec, which reconstruct extremely well but emit opaque integer tokens with no human meaning; and raw acoustic descriptor vectors, which are interpretable but live in a continuous space that neither people nor models communicate naturally. LAC's contribution is to quantize the descriptor vector into a lexical codebook and serialize it as natural-language text so that it is simultaneously readable, editable, grounded, and native to the text channel a model already speaks. The disagreement is narrow and it is about the carrier, not the idea. Natural orthographies were never designe