Why the Voynich Manuscript Might Not Be a Real Language

[quimqu]

Even if we don't strictly consider numbers, but rather some more generic 'enumeration' system, we will run into a problem that I see clearly in my mind, but may not be able to explain clearly.

The words okeey and qokeey can appear near each other, but differ only on the extreme left side of the word.

The words qokal and qokar are also similar and differ only on the extreme right. 

Using computer terminology, if this were an enumeration system, it would appear to be neither high-endian nor low-endian, but rather both-endian.

Dear René,

Your idea that the Voynich system may be “both-endian” — with variation at both the beginning and end of words — is a very compelling intuition. I explored this idea computationally by generating synthetic texts and comparing their character-level n-gram perplexity curves to Voynich and natural language texts.

Two simple generative models were tested:

1. Both-endian generator
Words are composed as:

prefix + core +suffix

• Prefixes and suffixes are drawn independently from small sets.
  
• Cores are also varied (e.g., oka, to, s, ly, or empty).
  
• Some short words (e.g. 1–3 characters) are allowed.
  

This setup creates clusters like: qokain, qokar, qoka, shoey, shoka, loin, etc.
These mirror the cases you mentioned — words that differ only at the extremes.

2. Phonotactic generator
Words follow abstract syllable patterns like CVC, CVVC, CVCC, etc., optionally surrounded by fixed prefixes and suffixes. This simulates a regular, artificial morphology without borrowing from real texts. The model is flexible enough to produce “both-endian” words — that is, words that vary at both the beginning and the end — since both the prefix and suffix are optional and randomly chosen. However, such combinations are less common due to how probabilities are distributed, making them emergent rather than dominant.

Perplexity curves:
I computed character-level n-gram perplexity under two settings:

• Full-text mode: the entire corpus is treated as a single character sequence.
  
• Reset-per-word mode: the model resets at each word boundary.
  

Here I attach the graphic results:


[size=1][/size]

When comparing Voynich texts, natural languages, and synthetic corpora, a key difference emerges in the shape of the perplexity curve under per-word reset.

In natural languages:

• Perplexity drops quickly at n=6–7 (reflecting phonotactic regularity).
  
• Then it stabilizes rapidly, forming a smooth, convex curve.
  
• There is no "hump" — the gain from longer n-grams becomes marginal.
  

In Voynich (EVA, CUVA) and the phonotactic model:

• There is a sharp drop at n=2, indicating strong local regularities.
  
• Then, a gradual decline over the next n values — forming a characteristic hump.
  
• The curve finally flattens near perplexity = 1, due to word length limits (most Voynich words <14 characters).
  

The both-endian model:

• Starts similarly, but the curve flattens too early, with minimal further gain.
  
• Its structure is too regular — the perplexity becomes stagnant.
  

The Voynich perplexity curve suggests:

• A system with strong intra-word regularity, like a constructed morphology.
  
• Enough internal variation to avoid early flattening (unlike purely mechanical systems).
  
• A structure not typical of natural language, but also not trivial enumeration.
  

The both-endian hypothesis aligns with what we see in the text: variation at both ends of words, combined with predictable cores. But the behavior of the perplexity curve shows that something richer is at play — possibly a controlled generative mechanism with constraints that mimic linguistic balance.

The fact that Voynich perplexity curves resemble those of rule-based phonotactic generators — more than natural languages or pure enumerations — supports the idea of a designed, internally consistent system, whether meaningful or not. I believe the shape of the perplexity curve — especially under reset per word — is a fingerprint of that structure.