phonemenal 0.1.1

General-purpose phonetic similarity detection — homophones, near-homophones, and sound-alike analysis

phonemenal

Phonetic similarity and homophone detection library for Python — near-homophones, sound-alike collisions, and variant generation.

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Features

• Three scoring algorithms (all normalized 0.0–1.0):
  • PPC-A — Positional Phoneme Correlation (Absolute)
  • PLD — Phoneme Levenshtein Distance at syllable level
  • LCS — Longest Common Subsequence ratio on phoneme sequences
• Composite scoring with configurable weights
• Exact homophone discovery via CMU Pronouncing Dictionary inversion
• Near-homophone search with threshold-based fuzzy matching
• Variant generation — phonetic substitutions, morphological variants, and separator permutations
• Compound word splitting with homophone permutation recombination
• Fast fallback encoder for words not in the dictionary (brand names, neologisms)
• Batch collision scanning — forward and reverse scanning pipelines
• LLM-powered deep analysis (optional, via Anthropic/OpenAI API or local agents)
• Rich CLI with formatted tables and JSON output

Install

pip install phonemenal

# With LLM support
pip install phonemenal[llm]

Quick Start

from phonemenal import similarity, homophones, variants, splitting, fallback, scanning

# Compare two words (all scores 0.0–1.0)
similarity.ppc("crowd", "crown")        # PPC-A
similarity.pld("elastic", "fantastic")   # PLD
similarity.lcs("packaging", "packages")  # LCS
similarity.composite("crowd", "crown")   # Weighted average

# Find exact homophones
homophones.find("blue")  # → ["blew"]

# Find near-homophones
homophones.find_similar("crowd", min_score=0.7)

# Generate sound-alike variants
variants.generate("flask")  # → {"phlask", "flazk", ...}
variants.generate_morphological("packaging")  # → {"packaged", "packager", ...}

# Split compound words & generate permutations
splitting.split("bluevoyage")  # → ["blue", "voyage"]
splitting.homophone_permutations("bluevoyage")  # → all recombinations

# Fallback for non-dictionary words
fallback.phonetic_key("numpy")   # → "nAmpY"
fallback.phonetic_key("numpie")  # → "nAmpY" (same key)

# Batch collision scanning
matches = scanning.scan(
    candidates=["numpie", "phlask"],
    known_names=["numpy", "flask"],
)

CLI

phonemenal similarity crowd crown           # compare with all algorithms
phonemenal similarity crowd crown -a ppc    # specific algorithm
phonemenal homophones blue                  # exact homophones
phonemenal variants flask -m                # phonetic + morphological variants
phonemenal split bluevoyage -p              # split & show permutations
phonemenal compare crowd crown              # full comparison report
phonemenal compare crowd crown -j           # JSON output
phonemenal analyze numpy --provider anthropic  # LLM deep analysis
phonemenal prompt numpy | pbcopy            # get raw prompt

Algorithms

PPC-A (Positional Phoneme Correlation — Absolute)

Builds positional phoneme combinations by traversing forward and reverse directions with padding, then measures set intersection. Captures how much of the positional phoneme structure two words share.

PLD (Phoneme Levenshtein Distance)

Syllable-level edit distance using the CMU dict's stress markers to split phonemes into syllable groups. Each syllable is an atomic unit, so the distance reflects how many whole syllables differ — matching how sound flows in speech.

LCS (Longest Common Subsequence)

Ratio of the longest common subsequence length to the total sequence length. Applied to phoneme sequences from CMU dict, or to raw character strings as a fallback.

Composite

Weighted average of all three algorithms, configurable per call. Default: equal weights. All bounded 0.0–1.0.

Fallback Encoder

Simplified Metaphone-inspired encoding for words not in the CMU dict. Applies digraph replacement, vowel normalization, and character collapsing to produce phonetic keys. Sound-alike names produce the same or similar keys — e.g. numpy and numpie both map to nAmpY.

Documentation

Full documentation is available at brokensound77.github.io/phonemenal.

License

Apache-2.0

Background and Research

This project stems from previous research on homophonic collisions conducted by Reagan Short and Justin Ibarra. You can check out our TROOPERS 2023 talk: Homophonic Collisions: Hold me closer Tony Danza for more background on the problem space and their approach to phonetic similarity detection.

• YouTube
• slides