PhonoPair scores are deterministic and explainable — not AI-generated
Every factor has a named source. Every score can be reproduced. This page explains exactly how the three-pillar scoring system works and the peer-reviewed research behind it.
PhonoPair does not use GPT, Claude, or any neural network to generate or score names. The scoring engine is a deterministic rule system built on top of structured linguistic databases. The same input will always produce the same output — and the output comes with a full breakdown explaining why.
This matters in professional naming contexts: you can show a client, investor, or legal team exactly why a name scored the way it did. There is no hallucination, no unexplainable confidence score, and no model drift over time.
Every word combination is scored across three independent pillars. The pillars are then blended into a final 0–100 score.
Alliteration, assonance, consonance, vowel shape compatibility, rhythm, syllable balance, and articulatory transition ease — the raw sound quality of a name pair.
Word type compatibility (adjective-noun, noun-noun), stress pattern harmony, syllable length preference, grammatical structure, and word order naturalness.
Meaning tension, cultural metonymy, conceptual contrast, oxymorons, onomatopoeia, and resonance — how the two words interact at a deeper level of meaning.
For a two-word combination, the final displayed score blends a per-word quality average (40%) with a pairwise compatibility score (60%). The compatibility score is the sum of all three pillar scores, clamped to 0–100.
These are structured databases and linguistic knowledge graphs — not language models. All sources are publicly documented and have been used in academic linguistics research.
Carnegie Mellon's machine-readable phonetic database mapping 135,000+ words to their ARPAbet phoneme sequences, syllable counts, and stress patterns.
Used for: All phonetic analysis — phonemes, syllables, stress, alliteration, rhymeA large-scale semantic network with over 34 million statements about how words relate — antonyms, associations, "is-a" relationships, and more.
Used for: Semantic factors — metonymy, oxymoron, complementarity, cultural resonanceA linguistics research API returning words by sound, spelling, meaning, and context — rhymes, collocations, sounds-like, and alliterations.
Used for: Phonetic matching, collocations, related wordsEncyclopedic cultural knowledge used to assess whether a word pair has real-world cultural significance beyond its literal meaning.
Used for: Cultural resonance and metonymy scoringAuthoritative American English dictionary providing definitions, part-of-speech tags, and etymology.
Used for: Language pillar — POS detection, grammar checksThe factors we score are not invented. Each one maps to peer-reviewed findings in psycholinguistics, cognitive psychology, and branding science.
Subjects shown alliterative word pairs recalled them significantly faster and more accurately. Effect held whether text was read silently or aloud.
Psychological Science, 2008 ↗Names whose sounds matched product attributes (sharp sounds for precise/fast products, round sounds for soft/warm products) scored higher on preference and purchase intent.
Journal of Consumer Research — Yorkston ↗Vowel position in the mouth (front vs. back) consistently predicts consumer perception of product size, speed, and personality — across English, Chinese, and Navajo speakers.
Multiple studies including ResearchGate / Journal of Global Marketing, 2023 ↗Rounded/soft sounds are perceived as friendly and approachable; angular/sharp sounds as precise and efficient. Brand phonetics that match visual identity score higher on coherence.
Bouba/kiki effect — 99designs, Wikipedia, multiple replication studies ↗Beautiful-sounding words tend to have 3+ syllables with first-syllable stress and liquid consonants (l, r, m, n). These preferences are implemented directly in PhonoPair's rhythm and consonant scoring.
David Crystal, "Phonaesthetically Speaking" (1995)Run the same name through PhonoPair tomorrow and get the same score. No model updates quietly changing your baselines.
Show a client, investor, or legal team exactly why a name scored the way it did — factor by factor, with source data.
Scores are directly comparable across names. A 78 means the same thing regardless of which names you ran before or after it.
Run a word pair through the analyzer and expand any factor card to see exactly which source and confidence level produced that score.