Adopt PanPhon realization channel

docs/ipa-vocal-tract-roadmap.md

@@ -236,8 +236,8 @@ The first Weksa handoff target is `weksa.utterance_embedding_handoff.v0.1`.
 AquaSynth should treat it as the training input contract:
 
 - 1024-float `bge-m3:latest` speech text embedding for semantic text evidence;
-- 256-float phonetic realization vector for IPA, phones, pronunciation, and
+- 256-float PanPhon-based phonetic realization vector for IPA, phones,
-  alien speech-shape evidence;
+  pronunciation, and alien speech-shape evidence;
 - 32-float deterministic prosody/emphasis hint vector;
 - 64-float projected Ghostlight/Epiphany-shaped character-state vector;
 - 64-float AquaSynth-owned learned utterance embedding output.

docs/utterance-parity-research.md

@@ -121,10 +121,10 @@ beautiful falsehood.
 Do not start serious gradient descent from toy embedding packets. The first
 training contract coordinated with Weksa is
 `weksa.utterance_embedding_handoff.v0.1`: 1024 speech-text floats from
-`bge-m3:latest`, 256 phonetic-realization floats for IPA/phones/alien speech
+`bge-m3:latest`, 256 PanPhon-based phonetic-realization floats for
-shape, 32 deterministic prosody/emphasis floats, 64 projected character-state
+IPA/phones/alien speech shape, 32 deterministic prosody/emphasis floats, 64
-floats, and a 64-float AquaSynth-owned utterance embedding output. Anything
+projected character-state floats, and a 64-float AquaSynth-owned utterance
-smaller is a plumbing fixture, not a training corpus.
+embedding output. Anything smaller is a plumbing fixture, not a training corpus.
 
 Keep semantic text and phonetic realization separate. English text embeddings
 may help preserve meaning and phrasing, but an IPA string is not English prose

src/AquaSynth.Core/SpeechDistributedTraining.cs

@@ -71,13 +71,14 @@ public sealed record SpeechTimingReceipt(
 [MessagePackObject]
 public sealed record SpeechUtteranceEmbeddingInputSnapshot(
     [property: Key(0)] float[] TextEmbedding,
-    [property: Key(1)] float[] Prosody,
+    [property: Key(1)] float[] PhoneticRealization,
-    [property: Key(2)] float[] CharacterState)
+    [property: Key(2)] float[] Prosody,
+    [property: Key(3)] float[] CharacterState)
 {
-    public UtteranceEmbeddingInput ToInput() => new(TextEmbedding, Prosody, CharacterState);
+    public UtteranceEmbeddingInput ToInput() => new(TextEmbedding, PhoneticRealization, Prosody, CharacterState);
 
     public static SpeechUtteranceEmbeddingInputSnapshot From(UtteranceEmbeddingInput input) =>
-        new([.. input.SpeechTextEmbedding], [.. input.ProsodyAndEmphasisHints], [.. input.CharacterStateVector]);
+        new([.. input.SpeechTextEmbedding], [.. input.PhoneticRealizationVector], [.. input.ProsodyAndEmphasisHints], [.. input.CharacterStateVector]);
 }
 
 [MessagePackObject]

src/AquaSynth.Core/UtteranceEmbeddingLearning.cs

@@ -2,13 +2,15 @@ namespace AquaSynth.Dsl;
 
 public sealed record UtteranceEmbeddingInput(
     IReadOnlyList<float> SpeechTextEmbedding,
+    IReadOnlyList<float> PhoneticRealizationVector,
     IReadOnlyList<float> ProsodyAndEmphasisHints,
     IReadOnlyList<float> CharacterStateVector)
 {
     public UtteranceEmbeddingFeatureVector ToFeatureVector()
     {
-        var values = new float[SpeechTextEmbedding.Count + ProsodyAndEmphasisHints.Count + CharacterStateVector.Count];
+        var values = new float[SpeechTextEmbedding.Count + PhoneticRealizationVector.Count + ProsodyAndEmphasisHints.Count + CharacterStateVector.Count];
         var offset = Copy(SpeechTextEmbedding, values, 0);
+        offset = Copy(PhoneticRealizationVector, values, offset);
         offset = Copy(ProsodyAndEmphasisHints, values, offset);
         Copy(CharacterStateVector, values, offset);
         return new UtteranceEmbeddingFeatureVector(values);
@@ -25,6 +27,35 @@ public sealed record UtteranceEmbeddingInput(
     }
 }
 
+public static class WeksaUtteranceEmbeddingHandoff
+{
+    public const string SchemaVersion = "weksa.utterance_embedding_handoff.v0.1";
+    public const string SpeechTextEmbeddingModelId = "bge-m3:latest";
+    public const string PhoneticRealizationModelId = "weksa.panphon_realization.v0.1";
+    public const int SpeechTextEmbeddingSize = 1024;
+    public const int PhoneticRealizationVectorSize = 256;
+    public const int ProsodyAndEmphasisHintSize = 32;
+    public const int CharacterStateVectorSize = 64;
+    public const int UtteranceEmbeddingSize = 64;
+    public const int InputSize = SpeechTextEmbeddingSize + PhoneticRealizationVectorSize + ProsodyAndEmphasisHintSize + CharacterStateVectorSize;
+
+    public static void Validate(UtteranceEmbeddingInput input)
+    {
+        ValidateSize(input.SpeechTextEmbedding, SpeechTextEmbeddingSize, nameof(input.SpeechTextEmbedding));
+        ValidateSize(input.PhoneticRealizationVector, PhoneticRealizationVectorSize, nameof(input.PhoneticRealizationVector));
+        ValidateSize(input.ProsodyAndEmphasisHints, ProsodyAndEmphasisHintSize, nameof(input.ProsodyAndEmphasisHints));
+        ValidateSize(input.CharacterStateVector, CharacterStateVectorSize, nameof(input.CharacterStateVector));
+    }
+
+    private static void ValidateSize(IReadOnlyList<float> values, int expected, string name)
+    {
+        if (values.Count != expected)
+        {
+            throw new ArgumentException($"{SchemaVersion} requires {name} to have {expected} values", name);
+        }
+    }
+}
+
 public sealed record UtteranceEmbeddingFeatureVector(IReadOnlyList<float> Values);
 
 public sealed record UtteranceEmbedding(IReadOnlyList<float> Values)

state/evidence.jsonl

@@ -122,4 +122,4 @@
 {"date":"2026-05-20","topic":"utterance-parity-current-objective","lesson":"AquaSynth's active objective is now the utterance parity curriculum: structured utterance metadata lowers into a learned utterance embedding, then into learned synth automation for the vocal tract and associated envelopes/LFOs/filters/spectral lanes, then into rendered speech loss. Broad DX7/Zyn synth digestion is backburner work unless it directly improves this harness.","evidence":"Updated README, docs/audio-doctrine.md, docs/reference-synth-roadmap.md, docs/ipa-vocal-tract-roadmap.md, state/map.yaml, state/spine.yaml, and state/memory.json to make the speech parity harness the current center."}
 {"date":"2026-05-20","topic":"utterance-parity-prior-work","lesson":"Prior work supports AquaSynth's architecture when kept in its lane: Kelly-Lochbaum/Pink Trombone and Story area functions support tract/area authority; TADA/articulatory phonology supports overlapping gestures; eSpeak/Klatt-style formant synthesis is useful fast ground truth but not anatomy; DDSP/neural source-filter work supports learned controllers over interpretable DSP rather than black-box throat replacement.","evidence":"Added docs/utterance-parity-research.md with source ledger covering Pink Trombone, VocalTractLab, Story, TADA, eSpeak NG, PanPhon, PHOIBLE, DDSP, DDSP articulatory vocoders, neural source-filter models, and LF voice-source references."}
 {"date":"2026-05-20","topic":"speech-training-time-witness","lesson":"Speech training receipts need time and confidence as evidence, not only loss. Each curriculum step should record when the stage decided, observed latency, intended latency budget, confidence, and artifact/model identifiers; a good-sounding result from an unbounded clockless path is not trustworthy.","evidence":"Added SpeechTimingReceipt to SpeechRenderRequest, SpeechRenderResult, and SpeechTrainingCheckpoint; seeded timing/confidence receipts in the local distributed-training proof; updated audio doctrine, IPA roadmap, utterance parity research notes, spine, and memory."}
-{"date":"2026-05-20","topic":"weksa-utterance-embedding-contract","lesson":"Do not start serious AquaSynth speech gradient descent from toy Weksa vectors, and do not treat semantic text embeddings as IPA embeddings. The coordinated Weksa handoff contract is `weksa.utterance_embedding_handoff.v0.1`: 1024 `bge-m3:latest` semantic speech-text floats, 256 phonetic-realization floats for IPA/phones/alien speech shape, 32 prosody/emphasis floats, 64 projected character-state floats, and a 64-float AquaSynth-owned utterance embedding output.","evidence":"Updated E:/Projects/weksa docs/schema/state to split speech text embedding from phonetic realization and marked the existing Nibu toy fixture as non-training; mirrored the training block in AquaSynth IPA roadmap, utterance parity research notes, spine, and memory."}
+{"date":"2026-05-20","topic":"weksa-utterance-embedding-contract","lesson":"Do not start serious AquaSynth speech gradient descent from toy Weksa vectors, and do not treat semantic text embeddings as IPA embeddings. The coordinated Weksa handoff contract is `weksa.utterance_embedding_handoff.v0.1`: 1024 `bge-m3:latest` semantic speech-text floats, 256 PanPhon-based phonetic-realization floats for IPA/phones/alien speech shape, 32 prosody/emphasis floats, 64 projected character-state floats, and a 64-float AquaSynth-owned utterance embedding output.","evidence":"Updated E:/Projects/weksa docs/schema/state to split speech text embedding from PanPhon-based phonetic realization and marked the existing Nibu toy fixture as non-training; mirrored the training block in AquaSynth IPA roadmap, utterance parity research notes, spine, and memory."}

state/memory.json

@@ -92,7 +92,7 @@
   "ipa_vocal_tract_path": {
     "status": "This is now the active project center. Roadmap and research notes align the curriculum around structured utterance metadata -> learned utterance embedding -> learned synth automation -> vocal tract/envelope/LFO/filter/spectral controls -> rendered audio loss. The first Core contracts still defend the boundary: PhoneticIntent is the Weksa/language-owned handoff, ArticulatoryPlan is the AquaSynth realization layer, VocalTractMorphology declares anatomy capabilities, ArticulatoryConstraintReport is the host-visible veto surface, and VocalTractPlanResult is the tiny end-to-end proof artifact for host branching. IPA is an authoring input only: tokenization and feature mapping lower into language phonology profiles, timed articulatory gestures, morphology-specific tract/excitation curves, and Faust DSP. The staged support plan starts with tokenization and vowels, then core pulmonic consonants, expanded human IPA, airstream/phonation, and Weksa/alien morphologies. Optional eSpeak NG tests provide the first generated speech workout when ESPEAK_NG or espeak-ng is installed; eSpeak is intelligibility/timing/log-mel pressure, not anatomy truth. AquaSynth owns the learned utterance embedding encoder and synth driver: speech text embeddings plus prosody/emphasis hints plus Ghostlight/Epiphany-shaped character state vectors compress into utterance embeddings, and phonetic features plus that embedding feed a packed-buffer native C# backprop/Adam mapper that predicts tract, AM/FM, LFO, filter, and mel spectral-envelope controls. SpeechBackpropagationPipeline chains the two models: synth-driver target loss returns gradients with respect to its input, slices the utterance-embedding gradient, and updates the utterance encoder from downstream speech loss. The generated ground-truth training fixture renders eSpeak syllables, extracts log-mel evidence, and trains that chained pipeline toward eSpeak-seeded mel/control targets.",
     "lesson": "Do not build a complete IPA parser before the tract can say basic syllables. The durable machine is tokenizer -> feature mapper -> phonology profile -> gesture planner -> morphology -> tract plan -> Faust renderer, with dependency-injected seams and mocks at each boundary. Weksa should use IPA-like phonetic input without letting human IPA semantics own alien anatomy. Anatomy has veto power: if a morphology lacks bilabial closure or another required capability, AquaSynth reports that failure instead of smearing over it.",
-    "next": "Do not start serious speech gradient descent from toy embedding packets. Mirror Weksa `weksa.utterance_embedding_handoff.v0.1` first: 1024 bge-m3 semantic speech-text floats, 256 phonetic-realization floats for IPA/phones/alien speech shape, 32 prosody/emphasis floats, 64 projected character-state floats, and 64 AquaSynth utterance-embedding output floats. Keep semantic text embeddings and phonetic realization separate. Then tighten the parity harness: render tiny utterance fixtures, sweep controller outputs through compiled Faust controls, score log-mel/timing/envelope loss, record per-stage decision time/latency/budget/confidence receipts, backpropagate into the utterance encoder plus synth driver, and connect Stage 0/1 IPA token spans, vowel feature bundles, and human baseline tract-area targets to that loop."
+    "next": "Do not start serious speech gradient descent from toy embedding packets. Mirror Weksa `weksa.utterance_embedding_handoff.v0.1` first: 1024 bge-m3 semantic speech-text floats, 256 PanPhon-based phonetic-realization floats for IPA/phones/alien speech shape, 32 prosody/emphasis floats, 64 projected character-state floats, and 64 AquaSynth utterance-embedding output floats. Keep semantic text embeddings and phonetic realization separate. Then tighten the parity harness: render tiny utterance fixtures, sweep controller outputs through compiled Faust controls, score log-mel/timing/envelope loss, record per-stage decision time/latency/budget/confidence receipts, backpropagate into the utterance encoder plus synth driver, and connect Stage 0/1 IPA token spans, vowel feature bundles, and human baseline tract-area targets to that loop."
   },
   "dx7_reference_path": {
     "status": "DX7 SysEx parser extracts voice edit buffers, packed voices, packed 32-voice banks, six operators, envelopes, algorithm, feedback, oscillator sync, LFO, transpose, voice name, checksum validation, ReferencePatch features, algorithm topology from the reverse-engineered OPS algorithm ROM table, envelope-to-ADSR approximations, measured carrier output amplitude, isolated two-op modulation route scale, summed/cascaded route scale, calibrated feedback amount table, ratio detune lowering, voice transpose lowering, fixed-frequency operator lowering, ROM COM output compensation cut, operator key/rate scaling at the effective played note, topology-aware route lowering, staged operator envelopes with per-segment curves, raw and block-interpolated DX7 envelope tracing, applied-envelope curved lowering with optional sustain floor, two AquaSynth DSL rebuild attempts, first-class OperatorGraph support with graph-level vibrato, public-domain SysEx fixture coverage, dexed-py reference rendering, project-authored Dexed patch rendering, AquaSynth candidate rendering through Faust-generated C#, one thresholded rendered parity test for analog1.syx voice 13, a hard PRC SYNTH1 parity probe with listening WAV artifacts and log-mel <= 0.145, project-authored algorithm-8 summed-stack/cascade/summed-pair parity, rendered community parity for Mooger, Piano Bass, RES SYNTH1, and ANLGSYN 1 with a sustained high-band gate, a MELLOWSOLO pressure artifact test, and renderable delayed operator feedback.",

state/spine.yaml

@@ -112,7 +112,7 @@ live_invariants:
   - eSpeak NG is an early parity reference for timing, IPA coverage, and log-mel pressure, not an anatomy oracle or final target.
   - Do not expand into prosody, emotional context, personality, or broad Weksa expressivity until the tiny IPA utterance harness can show useful loss movement.
   - Speech training receipts must keep time as a witness: every request, render result, and checkpoint should record stage decision time, latency spent, latency budget, confidence, and artifact/model identifiers.
-  - Serious speech gradient descent is blocked until Weksa packets use `weksa.utterance_embedding_handoff.v0.1` or a deliberate successor: 1024 semantic speech-text floats, 256 phonetic-realization floats, 32 prosody/emphasis floats, 64 projected character-state floats, and a 64-float AquaSynth utterance embedding output.
+  - Serious speech gradient descent is blocked until Weksa packets use `weksa.utterance_embedding_handoff.v0.1` or a deliberate successor: 1024 semantic speech-text floats, 256 PanPhon-based phonetic-realization floats, 32 prosody/emphasis floats, 64 projected character-state floats, and a 64-float AquaSynth utterance embedding output.
   - Do not use semantic text embeddings as a proxy for IPA or alien phonetic strings. Phonetic/IPA realization is a separate input channel.
 
 reference_targets:

tests/AquaSynth.Dsl.Tests/EspeakNgGradientDescentTests.cs

@@ -89,8 +89,19 @@ public sealed class EspeakNgGradientDescentTests
     private static TrainingFixture Fixture(string id, string text, PhoneticFeatures features, IReadOnlyList<float> textEmbedding)
     {
         var prosody = new[] { 0.75f, text.Length / 4f, features.Manner == PhoneticManner.Fricative ? 0.95f : 0.35f, features.Manner == PhoneticManner.Nasal ? 0.80f : 0.25f };
+        var phoneticRealization = new[]
+        {
+            features.Manner == PhoneticManner.Vowel ? 1f : 0f,
+            features.Manner == PhoneticManner.Stop ? 1f : 0f,
+            features.Manner == PhoneticManner.Fricative ? 1f : 0f,
+            features.Manner == PhoneticManner.Nasal ? 1f : 0f,
+            features.Place == PhoneticPlace.Bilabial ? 1f : 0f,
+            features.Place == PhoneticPlace.Alveolar ? 1f : 0f,
+            features.Place == PhoneticPlace.Velar ? 1f : 0f,
+            features.Phonation == Phonation.Voiced ? 1f : 0f
+        };
         var characterState = new[] { 0.65f, 0.35f, 0.55f, features.Place == PhoneticPlace.Bilabial ? 0.72f : 0.28f };
-        var input = new UtteranceEmbeddingInput(textEmbedding, prosody, characterState);
+        var input = new UtteranceEmbeddingInput(textEmbedding, phoneticRealization, prosody, characterState);
         var phoneticEvent = new PhoneticEvent(id, text, features, DurationSeconds: 0.20, Prosody: new PhoneticProsody(Stress: 0.75f, Intensity: 0.8f));
         return new TrainingFixture(id, text, "en", phoneticEvent, input);
     }

tests/AquaSynth.Dsl.Tests/SpeechBackpropagationPipelineTests.cs

@@ -8,7 +8,7 @@ public sealed class SpeechBackpropagationPipelineTests
     public void SpeechPipelineBackpropagatesSynthDriverLossIntoUtteranceEncoder()
     {
         var utteranceEncoder = UtteranceEmbeddingNeuralEncoder.Create(
-            inputSize: 10,
+            inputSize: 13,
             embeddingSize: 16,
             hiddenLayerSizes: [48, 32, 24],
             seed: 211);
@@ -37,7 +37,7 @@ public sealed class SpeechBackpropagationPipelineTests
     public void SpeechPipelineAcceptsExternalRenderedLossGradient()
     {
         var utteranceEncoder = UtteranceEmbeddingNeuralEncoder.Create(
-            inputSize: 10,
+            inputSize: 13,
             embeddingSize: 8,
             hiddenLayerSizes: [24, 16],
             seed: 301);
@@ -50,7 +50,7 @@ public sealed class SpeechBackpropagationPipelineTests
         var example = Example(
             "a",
             new PhoneticFeatures(PhoneticManner.Vowel, Height: VowelHeight.Open, Backness: VowelBackness.Front),
-            new UtteranceEmbeddingInput([0.9f, 0.1f, 0.2f, 0.7f], [0.8f, 0.3f, 0.2f], [0.6f, 0.2f, 0.4f]),
+            new UtteranceEmbeddingInput([0.9f, 0.1f, 0.2f, 0.7f], [0.9f, 0.2f, 0.1f], [0.8f, 0.3f, 0.2f], [0.6f, 0.2f, 0.4f]),
             SmallTarget([0.9f, 0.7f, 0.3f, 0.1f]));
         var before = pipeline.Predict(example).ToVector(pipeline.SynthDriver.MelBandCount);
         var gradient = new float[pipeline.SynthDriver.OutputSize];
@@ -77,17 +77,17 @@ public sealed class SpeechBackpropagationPipelineTests
         Example(
             "a",
             new PhoneticFeatures(PhoneticManner.Vowel, Height: VowelHeight.Open, Backness: VowelBackness.Front),
-            new UtteranceEmbeddingInput([0.92f, 0.20f, 0.35f, 0.70f], [0.88f, 0.32f, 0.20f], [0.65f, 0.20f, 0.44f]),
+            new UtteranceEmbeddingInput([0.92f, 0.20f, 0.35f, 0.70f], [0.90f, 0.18f, 0.10f], [0.88f, 0.32f, 0.20f], [0.65f, 0.20f, 0.44f]),
             Target([0.82f, 0.74f, 0.66f, 0.58f, 0.50f, 0.42f, 0.34f, 0.28f, 0.22f, 0.18f, 0.16f, 0.14f])),
         Example(
             "sa",
             new PhoneticFeatures(PhoneticManner.Fricative, PhoneticPlace.Alveolar, Phonation.Voiceless),
-            new UtteranceEmbeddingInput([0.35f, 0.42f, 0.90f, 0.44f], [0.70f, 0.92f, 0.75f], [0.28f, 0.60f, 0.82f]),
+            new UtteranceEmbeddingInput([0.35f, 0.42f, 0.90f, 0.44f], [0.22f, 0.86f, 0.76f], [0.70f, 0.92f, 0.75f], [0.28f, 0.60f, 0.82f]),
             Target([0.12f, 0.16f, 0.22f, 0.34f, 0.48f, 0.62f, 0.76f, 0.88f, 0.94f, 0.90f, 0.82f, 0.72f])),
         Example(
             "ma",
             new PhoneticFeatures(PhoneticManner.Nasal, PhoneticPlace.Bilabial, Phonation.Voiced, Nasalized: true),
-            new UtteranceEmbeddingInput([0.72f, 0.52f, 0.24f, 0.64f], [0.62f, 0.40f, 0.32f], [0.74f, 0.80f, 0.36f]),
+            new UtteranceEmbeddingInput([0.72f, 0.52f, 0.24f, 0.64f], [0.70f, 0.60f, 0.82f], [0.62f, 0.40f, 0.32f], [0.74f, 0.80f, 0.36f]),
             Target([0.68f, 0.72f, 0.70f, 0.62f, 0.54f, 0.46f, 0.40f, 0.36f, 0.32f, 0.30f, 0.28f, 0.26f]))
     ];
 

tests/AquaSynth.Dsl.Tests/SpeechDistributedTrainingTests.cs

@@ -89,7 +89,7 @@ public sealed class SpeechDistributedTrainingTests
     private static SpeechBackpropagationPipeline Pipeline()
     {
         var utteranceEncoder = UtteranceEmbeddingNeuralEncoder.Create(
-            inputSize: 10,
+            inputSize: 13,
             embeddingSize: 8,
             hiddenLayerSizes: [24, 16],
             seed: 701);
@@ -106,17 +106,17 @@ public sealed class SpeechDistributedTrainingTests
         Example(
             "a",
             new PhoneticFeatures(PhoneticManner.Vowel, Height: VowelHeight.Open, Backness: VowelBackness.Front),
-            new UtteranceEmbeddingInput([0.92f, 0.20f, 0.35f, 0.70f], [0.88f, 0.32f, 0.20f], [0.65f, 0.20f, 0.44f]),
+            new UtteranceEmbeddingInput([0.92f, 0.20f, 0.35f, 0.70f], [0.90f, 0.18f, 0.10f], [0.88f, 0.32f, 0.20f], [0.65f, 0.20f, 0.44f]),
             Target([0.82f, 0.74f, 0.66f, 0.58f, 0.50f, 0.42f])),
         Example(
             "sa",
             new PhoneticFeatures(PhoneticManner.Fricative, PhoneticPlace.Alveolar, Phonation.Voiceless),
-            new UtteranceEmbeddingInput([0.35f, 0.42f, 0.90f, 0.44f], [0.70f, 0.92f, 0.75f], [0.28f, 0.60f, 0.82f]),
+            new UtteranceEmbeddingInput([0.35f, 0.42f, 0.90f, 0.44f], [0.22f, 0.86f, 0.76f], [0.70f, 0.92f, 0.75f], [0.28f, 0.60f, 0.82f]),
             Target([0.12f, 0.16f, 0.22f, 0.34f, 0.48f, 0.62f])),
         Example(
             "ma",
             new PhoneticFeatures(PhoneticManner.Nasal, PhoneticPlace.Bilabial, Phonation.Voiced, Nasalized: true),
-            new UtteranceEmbeddingInput([0.72f, 0.52f, 0.24f, 0.64f], [0.62f, 0.40f, 0.32f], [0.74f, 0.80f, 0.36f]),
+            new UtteranceEmbeddingInput([0.72f, 0.52f, 0.24f, 0.64f], [0.70f, 0.60f, 0.82f], [0.62f, 0.40f, 0.32f], [0.74f, 0.80f, 0.36f]),
             Target([0.68f, 0.72f, 0.70f, 0.62f, 0.54f, 0.46f]))
     ];
 

tests/AquaSynth.Dsl.Tests/UtteranceEmbeddingLearningTests.cs

@@ -5,16 +5,33 @@ namespace AquaSynth.Dsl.Tests;
 public sealed class UtteranceEmbeddingLearningTests
 {
     [Fact]
-    public void UtteranceEmbeddingInputPacksTextProsodyAndCharacterState()
+    public void UtteranceEmbeddingInputPacksTextPhoneticsProsodyAndCharacterState()
     {
         var input = new UtteranceEmbeddingInput(
             SpeechTextEmbedding: [0.1f, 0.2f, 0.3f],
+            PhoneticRealizationVector: [0.9f, 0.6f],
             ProsodyAndEmphasisHints: [0.7f, 0.8f],
             CharacterStateVector: [0.4f, 0.5f, 0.6f]);
 
         var features = input.ToFeatureVector();
 
-        Assert.Equal([0.1f, 0.2f, 0.3f, 0.7f, 0.8f, 0.4f, 0.5f, 0.6f], features.Values);
+        Assert.Equal([0.1f, 0.2f, 0.3f, 0.9f, 0.6f, 0.7f, 0.8f, 0.4f, 0.5f, 0.6f], features.Values);
+    }
+
+    [Fact]
+    public void WeksaV01ContractRequiresSeparatePanphonRealizationChannel()
+    {
+        var input = new UtteranceEmbeddingInput(
+            SpeechTextEmbedding: Zeros(WeksaUtteranceEmbeddingHandoff.SpeechTextEmbeddingSize),
+            PhoneticRealizationVector: Zeros(WeksaUtteranceEmbeddingHandoff.PhoneticRealizationVectorSize),
+            ProsodyAndEmphasisHints: Zeros(WeksaUtteranceEmbeddingHandoff.ProsodyAndEmphasisHintSize),
+            CharacterStateVector: Zeros(WeksaUtteranceEmbeddingHandoff.CharacterStateVectorSize));
+
+        WeksaUtteranceEmbeddingHandoff.Validate(input);
+
+        Assert.Equal("weksa.utterance_embedding_handoff.v0.1", WeksaUtteranceEmbeddingHandoff.SchemaVersion);
+        Assert.Equal("weksa.panphon_realization.v0.1", WeksaUtteranceEmbeddingHandoff.PhoneticRealizationModelId);
+        Assert.Equal(1376, input.ToFeatureVector().Values.Count);
     }
 
     [Fact]
@@ -103,4 +120,6 @@ public sealed class UtteranceEmbeddingLearningTests
 
     private static float Lerp(float left, float right, float amount) =>
         left + (right - left) * amount;
+
+    private static float[] Zeros(int count) => new float[count];
 }