Task: Investigate the inverse decoder: audio → VoiceScenario

Goal

Test the hypothesis of the practical reversibility of our parametric model:

VoiceScenario → generator → audio

and vice versa:

audio → decoder → VoiceScenario.

Key verification principle

Start not with human speech, but with our own scenarios where the correct answer is known in advance.

For a scenario S:

S → G(S) = X

then:

X → D(X) = S'

The first rigorous criterion:

if the decoder is unable to reliably reconstruct the original scenarios from the audio created by our own generator, speaking of decoding real speech is premature.

Verification must be performed on the entire available set of different scenarios, rather than on a single successful case.

Stage 1. Synthetic ground truth

Gather a set of existing and new scenarios:

• sustained vocal sounds;
• noise;
• plosives;
• transitions;
• syllables/fragments;
• short words.

For each, store:

• original JSON;
• exact generated audio;
• detailed analytical log;
• decoder result;
• differences between the original and reconstructed scenario;
• result of re-synthesis from the reconstructed scenario.

We need to understand which parameters are restored reliably, which are ambiguous, and what log resolution is necessary.

Stage 2. Re-synthesis verification

It is not enough to get a similar JSON. The reconstructed scenario must be played back again:

S → X → D(X)=S' → G(S')=X'

And compare:

• scenario structure;
• analytical features of X and X';
• human auditory perception.

Stage 3. Transfer to human speech

Only after stable performance on our own synthetic data should we move on to recording a human.

The goal here is no longer necessarily to reconstruct some "true" physical human scenario, which we do not have. Practical criterion:

the decoder creates a VoiceScenario which, when played back, produces a recognizable and sufficiently close human sound.

Main open question

To what extent the space of human speech is covered by the current audio space of our generator.

AudioContext is technically capable of reproducing any digital waveform if given a full array of samples. But our system intentionally replaces thousands of samples with a compact structural description.

Therefore, we are investigating not the possibility of reproduction in general, but the boundary:

what is the minimum complexity of the structural representation sufficient for an acceptable quality of human speech?

Potential model evolution

If the current VoiceScenario is not enough, do not consider this an automatic refutation of the approach. Explore a gradual increase in expressiveness:

• richer source model;
• additional resonances/anti-resonances;
• turbulence/noise;
• local spectral details;
• denser temporal control points;
• short residual components if necessary.

Ultimately, the representation can approach a waveform. The practical goal is to find the point where the quality is already sufficient, while the description is still significantly more compact and structural than an array of samples.

Connection with the editor

Even an imperfect decoder can be useful if it produces a good draft scenario:

recording → draft VoiceScenario → visual editor/AI → human refines by ear.

Success criteria

1. The decoder reliably restores its own scenarios at least on a limited set of sound classes.
2. Re-synthesis from the restored scenario is perceptually close to the original synthetic sound.
3. It is clear what data is lost during the current analysis and whether log compression needs to be reduced.
4. On real recordings, at least useful starting scenarios are obtained.
5. It is possible to estimate the dependency quality ↔ complexity/representation size and approach the practical boundary of the system.