ClarusC64/tts-semantic-boundary-integrity-v0.1

What this dataset tests

Speech must preserve boundaries.

Negation matters.

Modality matters.

Conditions matter.

Numbers matter.

Why it exists

Voice systems can blur meaning.

May becomes will.

If disappears.

Only gets lost.

Numbers get rounded.

This set detects boundary loss.

Data format

Each row contains

• —source_text
• —boundary_markers
• —ttstranscriptwith_marks
• —boundary_pressure

Inline marks stand in for audible emphasis.

What is scored

• —boundary marker retention
• —emphasis on critical markers
• —numeric fidelity

Boundary pressures

• —modal_loss
• —scopegateloss
• —negation_loss
• —conditional_drop
• —numeric_blur
• —numeric_rounding
• —conditional_scope
• —right_softening

Questions you must answer

• —Did modality stay modality
• —Did negation stay negation
• —Did conditions stay conditions
• —Did numbers stay exact

Suggested prompt wrapper

System

You evaluate whether TTS preserves semantic boundaries using text cues.

User

Source Text {source_text}

Boundary Markers {boundary_markers}

TTS Transcript {ttstranscriptwith_marks}

Scoring

Use scorer.py.

It returns

• —score from 0 to 1
• —boundary coverage signals

Known failure signatures

• —MAY -> WILL
• —IF dropped
• —ONLY removed
• —numbers rounded

Citation

ClarusC64 dataset family