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Phonological Similarity

Find phonologically similar words with adjustable weights for onset, nucleus, and coda components.

Overview

The Phonological Similarity tool uses phoneme-sequence soft Levenshtein distance to find similar words while preserving: - Consonant clusters (e.g., /kr/, /st/) - Diphthongs (e.g., /aɪ/, /oʊ/) - Syllable structure

Basic Usage

  1. Enter a target word
  2. Choose a preset or set custom weights
  3. Adjust threshold (how similar words must be)
  4. Set limit (maximum results)
  5. Click Find Similar Words

Weight Presets

Rhymes

  • Onset: 0.0, Nucleus: 0.5, Coda: 0.5
  • Matches nucleus and coda sounds
  • Example: "cat" → bat, hat, sat, mat

Alliteration

  • Onset: 1.0, Nucleus: 0.0, Coda: 0.0
  • Matches initial sounds only
  • Example: "cat" → can, cap, cast, kit

Assonance

  • Onset: 0.0, Nucleus: 1.0, Coda: 0.0
  • Matches vowel sounds only
  • Example: "cat" → bad, had, slam

Consonance

  • Onset: 0.5, Nucleus: 0.0, Coda: 0.5
  • Matches consonants, ignores vowels
  • Example: "cat" → kit, cot, cut

Balanced

  • Onset: 0.33, Nucleus: 0.33, Coda: 0.33
  • Considers all components equally
  • Example: "cat" → similar overall sound

Understanding Similarity Scores

Scores range from 0.0 (completely different) to 1.0 (identical):

  • 0.90+: Perfect rhymes (cat-bat)
  • 0.75-0.89: Very similar (cat-cap)
  • 0.60-0.74: Moderately similar (cat-crest)
  • < 0.60: Somewhat different

Custom Weights

Adjust sliders to create your own similarity definition:

  • Increase onset for more initial sound matching
  • Increase nucleus for more vowel matching
  • Increase coda for more final sound matching

Threshold Control

  • High threshold (0.85+): Only very similar words
  • Medium threshold (0.70-0.84): Moderately similar words
  • Low threshold (< 0.70): Broader matches

Technical Architecture

Phoneme-Sequence Representation

PhonoLex represents words as sequences of syllables, with each syllable containing three components:

Word: "cat" /kæt/
Syllable 1:
  onset = [[k]]      (sequence of 1 phoneme vector: 76-dim)
  nucleus = [[æ]]    (sequence of 1 phoneme vector: 76-dim)
  coda = [[t]]       (sequence of 1 phoneme vector: 76-dim)

Word: "crest" /kɹɛst/
Syllable 1:
  onset = [[k], [ɹ]]   (sequence of 2 phoneme vectors)
  nucleus = [[ɛ]]      (sequence of 1 phoneme vector)
  coda = [[s], [t]]    (sequence of 2 phoneme vectors)

Key Insight: Consonant clusters and diphthongs are preserved as sequences of phoneme vectors, not averaged into single vectors. This allows proper discrimination of: - Different cluster lengths ("cat" vs "crest") - Different syllable structures ("cat" vs "act") - Complex phoneme patterns ("spray" vs "say")

Similarity Computation Algorithm

Phonological similarity is computed using soft Levenshtein distance on phoneme sequences with weighted syllable components.

Algorithm Overview: 1. Decompose both words into syllables (onset-nucleus-coda) 2. For each syllable pair, compute component distances: - Onset distance (soft Levenshtein on onset phoneme sequences) - Nucleus distance (soft Levenshtein on nucleus phoneme sequences) - Coda distance (soft Levenshtein on coda phoneme sequences) 3. Combine component distances using user-specified weights 4. Average across all syllable pairs 5. Convert distance to similarity: similarity = 1 - distance

Soft Levenshtein Distance

Soft Levenshtein extends standard edit distance to use phoneme vector similarity instead of exact matches.

Standard Levenshtein: Counts insertions, deletions, substitutions - /k/ vs /t/: cost = 1 (substitution) - /k/ vs /k/: cost = 0 (match)

Soft Levenshtein: Uses cosine similarity between phoneme vectors - /k/ vs /t/: cost = 1 - sim(k, t) = 1 - 0.65 = 0.35 - /k/ vs /g/: cost = 1 - sim(k, g) = 1 - 0.92 = 0.08 (voicing difference only) - /k/ vs /k/: cost = 1 - 1.0 = 0.0 (identical)

Algorithm (for two phoneme sequences A and B): 

def soft_levenshtein(A, B):
    # Initialize distance matrix
    D[i][0] = sum of insertion costs for A[0:i]
    D[0][j] = sum of insertion costs for B[0:j]

    # Fill matrix
    for i in 1 to len(A):
        for j in 1 to len(B):
            # Substitution cost based on phoneme similarity
            subst_cost = 1 - cosine_similarity(A[i], B[j])

            D[i][j] = min(
                D[i-1][j] + 1,              # deletion
                D[i][j-1] + 1,              # insertion
                D[i-1][j-1] + subst_cost    # substitution
            )

    # Normalize by maximum possible distance
    return D[len(A)][len(B)] / max(len(A), len(B))

Why This Works: - Insertion/deletion cost = 1.0 (penalizes length differences) - Substitution cost varies (0.0 to 1.0) based on phoneme similarity - Normalization ensures output in [0, 1] range - Similar phonemes have lower substitution costs

Weight Application

User-specified weights control the importance of each syllable component.

Weight Formula: 

weighted_distance = (w_onset × onset_dist + w_nucleus × nucleus_dist + w_coda × coda_dist) / (w_onset + w_nucleus + w_coda)

Presets:

Preset Onset Nucleus Coda Effect
Rhymes 0.0 0.5 0.5 Ignores onset, prioritizes nucleus+coda
Alliteration 1.0 0.0 0.0 Only onset matters
Assonance 0.0 1.0 0.0 Only nucleus (vowel) matters
Consonance 0.5 0.0 0.5 Ignores vowels, prioritizes consonants
Balanced 0.33 0.33 0.33 All components equally weighted

Worked Example: "cat" vs "bat"

Step 1: Syllabification 

"cat" /kæt/: onset=[k], nucleus=[æ], coda=[t]
"bat" /bæt/: onset=[b], nucleus=[æ], coda=[t]

Step 2: Component Distances (using Rhymes preset: onset=0.0, nucleus=0.5, coda=0.5)

Onset distance: - Soft Levenshtein([k], [b]) = 0.35 (some similarity, both stops)

Nucleus distance: - Soft Levenshtein([æ], [æ]) = 0.0 (identical)

Coda distance: - Soft Levenshtein([t], [t]) = 0.0 (identical)

Step 3: Weighted Distance 

weighted_distance = (0.0 × 0.35 + 0.5 × 0.0 + 0.5 × 0.0) / (0.0 + 0.5 + 0.5)
                  = 0.0 / 1.0
                  = 0.0

Step 4: Convert to Similarity 

similarity = 1 - weighted_distance = 1 - 0.0 = 1.0

Result: "cat" and "bat" have similarity 1.0 with Rhymes preset (perfect rhyme, onset ignored).

Worked Example: "cat" vs "crest"

Step 1: Syllabification 

"cat" /kæt/: onset=[k], nucleus=[æ], coda=[t]
"crest" /kɹɛst/: onset=[k, ɹ], nucleus=[ɛ], coda=[s, t]

Step 2: Component Distances (using Balanced preset: all weights = 0.33)

Onset distance: - Soft Levenshtein([k], [k, ɹ]) - Matrix calculation: 

D[0][0] = 0
D[1][0] = 1 (delete k from A)
D[0][1] = 1 (insert k to B)
D[0][2] = 2 (insert k, ɹ to B)
D[1][1] = min(D[0][1]+1, D[1][0]+1, D[0][0]+(1-sim(k,k))) = min(2, 2, 0) = 0
D[1][2] = min(D[0][2]+1, D[1][1]+1, D[0][1]+(1-sim(k,ɹ))) = min(3, 1, 1.75) = 1
- Distance = 1 / max(1, 2) = 0.5

Nucleus distance: - Soft Levenshtein([æ], [ɛ]) - Both are front vowels, high similarity - Distance ≈ 0.15

Coda distance: - Soft Levenshtein([t], [s, t]) - Matrix calculation: 

D[1][2] = 1 (insert s)
- Distance = 1 / max(1, 2) = 0.5

Step 3: Weighted Distance 

weighted_distance = (0.33 × 0.5 + 0.33 × 0.15 + 0.33 × 0.5) / (0.33 + 0.33 + 0.33)
                  = (0.165 + 0.0495 + 0.165) / 0.99
                  = 0.3795 / 0.99
                  ≈ 0.38

Step 4: Convert to Similarity 

similarity = 1 - 0.38 = 0.62

Result: "cat" and "crest" have similarity ~0.62 (moderately similar, penalized for cluster differences).

Worked Example: "cat" vs "act"

Step 1: Syllabification 

"cat" /kæt/: onset=[k], nucleus=[æ], coda=[t]
"act" /ækt/: onset=[], nucleus=[æ], coda=[k, t]

Step 2: Component Distances (using Balanced preset)

Onset distance: - Soft Levenshtein([k], []) - Distance = 1 / max(1, 0) = 1.0 (complete mismatch)

Nucleus distance: - Soft Levenshtein([æ], [æ]) = 0.0 (identical)

Coda distance: - Soft Levenshtein([t], [k, t]) - Distance ≈ 0.5 (extra phoneme)

Step 3: Weighted Distance 

weighted_distance = (0.33 × 1.0 + 0.33 × 0.0 + 0.33 × 0.5) / 0.99
                  = (0.33 + 0 + 0.165) / 0.99
                  = 0.495 / 0.99
                  ≈ 0.50

Step 4: Convert to Similarity 

similarity = 1 - 0.50 = 0.50

Result: "cat" and "act" have similarity ~0.50 (anagrams properly distinguished due to different syllable structures).

Score Interpretation Guide

Understanding what similarity scores mean in different contexts:

Perfect Matches (1.0)

  • Identical words (same phonemes, same positions)
  • Perfect rhymes with Rhymes preset (cat-bat: 1.0)
  • Perfect alliteration with Alliteration preset (cat-cap: ~0.95+)

Very High Similarity (0.85-0.99)

  • Perfect rhymes (cat-hat: 0.95)
  • One-phoneme substitutions in similar classes (cat-cap: 0.88)
  • Minimal vowel differences (cat-cot: 0.90)

High Similarity (0.70-0.84)

  • Imperfect rhymes (cat-cap: 0.78)
  • Similar clusters (spray-splay: 0.75)
  • Vowel + coda matches (cat-bad: 0.72)

Moderate Similarity (0.55-0.69)

  • Shared onset + nucleus (cat-cab: 0.65)
  • One component matches well (cat-crest: 0.62)
  • Similar phoneme distributions (cat-cut: 0.60)

Low Similarity (0.40-0.54)

  • Anagrams with different syllable structures (cat-act: 0.50)
  • Some shared phonemes (cat-talk: 0.45)
  • Length differences (cat-catastrophe: 0.42)

Very Low Similarity (< 0.40)

  • Completely different phonemes (cat-dog: 0.25)
  • Different syllable counts (cat-computer: 0.20)
  • No shared components (cat-box: 0.15)

Note: Scores depend heavily on weight settings. The above ranges assume Balanced preset (all weights = 0.33).

Performance Characteristics

Operation Time Notes
Word lookup 5-10 ms O(1) hash lookup
Syllabification 1-2 ms O(n) where n = phoneme count
Soft Levenshtein (per component) 0.1-0.5 ms O(m × n) where m, n = sequence lengths
Full vocabulary scan 50-100 ms 44,011 comparisons
Top-20 results 60-120 ms Full scan + sort

Factors Affecting Speed: - Target word complexity (more syllables = slower) - Vocabulary size (more words = slower scan) - Browser performance (Chrome/Edge slightly faster)

Optimization: Results are computed on-the-fly (not cached). Changing weights requires recomputation.

Limitations and Edge Cases

Monosyllabic vs. Multisyllabic Comparisons

Comparing words with different syllable counts can produce unexpected results:

"cat" /kæt/ (1 syllable) vs "catalog" /kætəlɔg/ (3 syllables)

Result: Low similarity (~0.35) due to length penalty

Reason: Soft Levenshtein normalizes by maximum length, but syllable count differences are heavily penalized.

Empty Components

Words with missing onset or coda:

"act" /ækt/: onset=[] (no onset)
"see" /si/: coda=[] (no coda)

Comparing "act" vs "cat":
- Onset distance = 1.0 (empty vs [k])
- Nucleus distance = 0.0 (both [æ])
- Coda distance = 0.5 ([k,t] vs [t])

Effect: Empty components are treated as complete mismatches (distance = 1.0).

Diphthongs

Diphthongs are represented as sequences:

"time" /taɪm/: nucleus = [a, ɪ] (2 vectors)
"team" /tim/: nucleus = [i] (1 vector)

Nucleus distance ≈ 0.5 (length difference penalized)

Effect: Diphthongs vs. monophthongs incur insertion/deletion penalties.

Weight = 0.0 Does Not Mean "Ignore"

Zero weights still contribute to normalization:

Rhymes preset: onset=0.0, nucleus=0.5, coda=0.5

weighted_distance = (0.0 × onset_dist + 0.5 × nucleus_dist + 0.5 × coda_dist) / 1.0

Interpretation: Onset distance is computed but multiplied by 0, effectively ignoring it.

Stress and Syllable Alignment

Multi-syllabic words compare syllables pairwise:

"computer" /kəmpjutɚ/ (3 syllables)
"commuter" /kəmjutɚ/ (3 syllables)

Syllable 1: /kəm/ vs /kəm/ → high similarity
Syllable 2: /pju/ vs /ju/ → moderate similarity (extra /p/)
Syllable 3: /tɚ/ vs /tɚ/ → high similarity

Overall: ~0.80 (high similarity)

Note: Syllable alignment is position-based (1st syllable vs 1st syllable, etc.). No dynamic alignment.

Vocabulary Coverage

  • Full vocabulary: 44,011 words from CMU Pronouncing Dictionary
  • Dialect: General American English (primary pronunciations only)
  • Excluded: Pronunciation variants, proper nouns, non-English loanwords

Implication: Some words you search for may not be in the vocabulary.

Advanced Use Cases

Finding Imperfect Rhymes (Slant Rhymes)

Goal: Find words that rhyme approximately but not perfectly

Settings: - Target: "love" - Preset: Rhymes (onset=0.0, nucleus=0.5, coda=0.5) - Threshold: 0.70-0.85 (exclude perfect rhymes)

Expected Results: of, above, dove, shove, glove (varying vowel quality)

Finding Consonant Frames

Goal: Find words with similar consonant structure but different vowels

Settings: - Target: "big" - Preset: Consonance (onset=0.5, nucleus=0.0, coda=0.5) - Threshold: 0.75+

Expected Results: bag, bug, bog, beg (same /b_g/ frame)

Finding Phonetic Neighbors

Goal: Find words that sound overall similar (useful for phonological neighborhood density studies)

Settings: - Target: any word - Preset: Balanced (all=0.33) - Threshold: 0.70+ - Limit: 50

Expected Results: One-phoneme substitutions, additions, deletions

Custom Weight Exploration

Goal: Test hypotheses about phonological similarity

Example: "Does onset matter more than coda for word recognition?"

Settings: - Try onset=0.5, nucleus=0.25, coda=0.25 - Compare results to coda=0.5, nucleus=0.25, onset=0.25 - Observe which words appear in top results

Data Sources and Phoneme Vectors

Phoneme Vectors: 26-dimensional continuous vectors learned via Bayesian inference from empirical evidence: - 26 articulatory features with continuous values in [0, 1] - Learned from acoustic data, confusion corpora, and morphological patterns - r=0.987 cosine correlation with prior theory-assigned features

Syllabification: Based on English phonotactic constraints - Maximal onset principle - Sonority sequencing - Legal cluster identification

Coverage: 39 English phonemes (General American English from CMU Pronouncing Dictionary)

See Also