Match voice patterns across essays, chats, and emails. Tune weights for tone, punctuation, and formality. Turn raw text differences into clear adaptation steps today.
This sample shows how stylistic signals can differ between a conversational source and a more formal target reference.
| Sample | Words | Avg Sentence Length | Lexical Diversity | Formality Index | Contraction Rate |
|---|---|---|---|---|---|
| Project update draft | 34 | 17.00 | 76.40% | 44.00 | 5.80% |
| Policy summary target | 32 | 16.00 | 72.10% | 63.50 | 0.00% |
| Estimated match | 78.60% with stronger adjustment needed in formality and contractions. | ||||
The tool extracts measurable signals from both texts. These include sentence length, average word length, lexical diversity, punctuation density, formality, contractions, question usage, and pronoun usage.
Feature similarity = (1 − min((|Source − Target| ÷ Range) × Strictness, 1)) × 100
Overall match = Σ(Feature similarity × Weight) ÷ Σ(Weight)
Ranges keep each signal on a fair scale. Higher weights make a signal matter more. A higher strictness multiplier penalizes stylistic differences more aggressively.
It represents weighted similarity across selected style signals. A high score means the texts share similar rhythm, vocabulary texture, tone markers, and conversational habits.
No. It focuses on style resemblance, not strict grammar judgment. Clean grammar can help the measurement, but the score mainly reflects how similarly the texts sound.
Short samples contain fewer stable patterns. A tiny paragraph may distort sentence rhythm, punctuation frequency, and vocabulary variety, which can make the comparison less representative.
It controls how strongly differences reduce similarity. Lower strictness is forgiving. Higher strictness makes even moderate gaps count more against the final score.
Yes. Set that feature weight to zero. The formula will exclude its influence from the weighted average while still showing the feature in the comparison table.
This version measures style, not deep semantic meaning. Two texts can discuss different topics and still match well if their writing habits are similar.
Use CSV when you want raw values for spreadsheets or reporting. Use PDF when you want a shareable summary with the visible result cards and graph.
It is useful for lightweight screening, prompt testing, and editorial comparison. For production evaluation, combine it with semantic checks, human review, and task accuracy metrics.
Important Note: All the Calculators listed in this site are for educational purpose only and we do not guarentee the accuracy of results. Please do consult with other sources as well.