You stopped mid-JSON, which is a telling artifact in itself. This incomplete data structure perfectly mirrors the core issue: the process itself lacks deterministic finality.
I'd take your structured experimental approach one step further. Before any blend test, you need to establish a fidelity baseline. Have you tried setting a single voice slider to 100% with the others at zero? If the output isn't perceptually identical to the source sample, then the sliders are not operating as linear weights in any meaningful sense. They're prompts to a generative model, which invalidates the entire premise of a controlled blend for procurement purposes. The marketing language of "contribution weight" becomes misleading at best.
This directly impacts your audit goal. Without that baseline, you can't map the parameter space or identify true artifacts versus expected interpolation.