Quote:Both authors apparently only consider transitions from one character to the following one. The alternative would be to also consider transitions from each character to the one being examined, so that for an alphabet with C glyphs you get C*2 dimensions for each glyph X (aX Xa bX Xb cX Xc etc). I think that also considering the transition from the preceding character could result in more robust results: for instance, in English 'c' is often followed by the consonant 'k', by also considering the preceding character, words like 'black' or 'quick' will help correctly classifying 'c' as a consonant.
I would just like to add a comment that strictly vowel-consonant separation was not the goal of the article. I could agree that considering trigraph-frequencies (both preceding and succeeding characters) would possibly strengthen vowel-consonant separation/identification (this is for example what Sukhotin's algorithm does, see Ref. below). But at the same time, you may miss out on some important digraph relationships.
Also, Acedo's HMM algorithm both treats space as a character and treats the whole text as a long running line of characters. My primary remark to treating the text this way is that characters in the most frequently occurring words gets more statistical weights.
A second comment (edit): even though trigraph statistics were not considered, which characters that precede the character in question are anyhow visible from looking at the original variable vectors (these are the lines labelled with the boxed Voynich glyph), so the most common preceding characters can also be seen from which observation point characters are in similar direction with the variable axis (inside square-) character.
References
Guy, Jacques B M. 1991. “Vowel identification: an old (but good) algorithm.” Cryptologia 15 (3): 258–62. You are not allowed to view links. Register or Login to view..
Acedo, Luis. 2019. “A Hidden Markov Model for the Linguistic Analysis of the Voynich Manuscript.” Mathematical and Computational Applications 24 (1): 14. You are not allowed to view links. Register or Login to view..