Back to Language as Infrastructure
experiment2026Experiment harness

FAC Read-Speech Evaluation Harness

The FAC harness exists to test whether feature-level acoustic cues can be gathered and evaluated without confusing prompted audio, transcript truth, and training labels. It is deliberately fail-closed until reviewed labels exist.

Paper workspace

Live draft structure

exploratory

Artifacts

Experiment runbook

The harness is source and runbook first. No PDF is needed until a real read-speech result exists.

source-only

Editable source

Experiment runbook and code exist. It remains blocked on real read speech aligned to known toned N'Ko text.

Source anchors

nko-acoustic-coding/experiments/READ_SPEECH_RUNBOOK.md

nko-acoustic-coding/experiments/run_read_speech_eval.py

nko-acoustic-coding/Makefile

nko-acoustic-coding/experiments/tone_fusion_eval.py

nko-acoustic-coding/experiments/RESULTS.md

Method tags

read speechtone evaluationTDERFAC

Ingest intersections

facread-speechtonetderaudio-ground-truth

Status

Experiment scaffolded; not yet enough reviewed live labels for a correctness claim.

Key claims

01

Feature supervision must be collected separately from transcript guesses.

02

Prompted family audio can support coverage, not automatic correctness.

03

FAC should enter the model only after replayable reviewed evidence exists.

Public reading note

Public summary only; no accuracy claim yet.

Standard skeleton

What this paper must keep proving

Schema

problem

FAC needs a decisive acoustic tone evaluation, but lesson commentary cannot supply aligned tone ground truth.

method

Require a read WAV aligned to known tone-marked N'Ko text, then score tone-diacritic evidence instead of text fluency.

implementation

Makefile read-speech target, read-speech runbook, tone fusion evaluator, and gold-tone extraction logic.

data

Admissible data is read speech of known toned text. Lesson audio and OCR are useful context but not aligned acoustic truth.

evaluation

Tone-diacritic error rate and feature/tone fidelity after alignment.

references

Tone language ASR, pitch tracking, tone diacritic evaluation, read-speech corpus design.

openQuestions

Collecting the first small, clean read-speech packet with known toned N'Ko text.

Checkpoints and references

Proof chain

implementationproven

Harness source exists

nko-acoustic-coding read-speech target

The code path for running the evaluation exists.

datasetblocked

Real acoustic TDER

aligned read-speech WAV plus gold N'Ko text

The decisive metric is intentionally blocked until the right data exists.