Centralize utterance embedding learning
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@ -183,17 +183,19 @@ features become overlapping constriction tasks and source events. It should not
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linearly interpolate phone presets unless a test proves that shortcut works for
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the current stage.
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The first learned controller lives below the handoff and above rendering:
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`VocalTractNeuralMapper` takes phonetic features plus a semantic embedding vector
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and predicts tract controls. The default shape is deliberately small but real:
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three hidden dense layers with tanh activations, sigmoid output controls, and a
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native C# training path using packed row-major buffers, unsafe hot loops, batched
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gradients, and Adam or SGD updates. Its output includes core tract parameters plus
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expressive synthesis lanes: AM depth, FM depth, LFO rate/depth, filter cutoff,
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filter resonance, and a full mel-frequency spectral envelope vector for
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PAD-style wavetable/formant coloration. Those lanes are not license to bypass
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tract anatomy; they are renderable expression controls the later synth can
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choose to honor.
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The first learned controllers live below the handoff and above rendering. Weksa
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or another language layer may provide speech text embeddings, prosody/emphasis
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hints, and Ghostlight/Epiphany-shaped character state vectors, but AquaSynth owns
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the gradient descent. `UtteranceEmbeddingNeuralEncoder` trains a tiny model to
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compress those ingredients into one utterance embedding. `VocalTractNeuralMapper`
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then takes phonetic features plus that embedding and predicts tract controls.
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Both controllers use the same native C# packed-network path: row-major buffers,
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unsafe hot loops, batched gradients, and Adam or SGD updates. The synth-driver
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output includes core tract parameters plus expressive synthesis lanes: AM depth,
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FM depth, LFO rate/depth, filter cutoff, filter resonance, and a full
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mel-frequency spectral envelope vector for PAD-style wavetable/formant
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coloration. Those lanes are not license to bypass tract anatomy; they are
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renderable expression controls the later synth can choose to honor.
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### 4. Morphology Model
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