03-03-2025, 07:07 PM
A couple quick points:
* As someone associated with the Institute of Archaeological Sciences in Bern you almost certainly know people on the linguistics faculty, or at the very least know people who know people on the linguistics faculty. I would strongly advise identifying one or more expert linguists and running this by them.
* The most common words in any fairly large sample of text tend to be function words -- applying your mapping, is that the case here?
* I share the skepticism of some of the other posters that what you're suggesting will quantitatively shift the entropies enough to make the text look consistent with a natural language, but am open to persuasion by demonstration. This can be approached from either (ideally, both) of two directions:
1) Map (say) the first three quires from EVA to your proposed Latin alphabet equivalents and compare the h1 and h2 values to those of the types of texts you mined for vocabulary matches. I understand that there are some ambiguities in doing this -- should EVA 'ar' be read as "N" or "AR", for example -- but you have to start somewhere.
2) Take some of the contemporary texts you mined for vocabulary and convert them to EVA using your scheme (or better yet, to the Currier transcription alphabet, which also groups some of the oversplit EVA sequences to single characters) -- does doing that lower h1 & h2 to levels consistent with the Voynich text? There may be some difficulties automating this perfectly, but again you have to start somewhere.
* As someone associated with the Institute of Archaeological Sciences in Bern you almost certainly know people on the linguistics faculty, or at the very least know people who know people on the linguistics faculty. I would strongly advise identifying one or more expert linguists and running this by them.
* The most common words in any fairly large sample of text tend to be function words -- applying your mapping, is that the case here?
* I share the skepticism of some of the other posters that what you're suggesting will quantitatively shift the entropies enough to make the text look consistent with a natural language, but am open to persuasion by demonstration. This can be approached from either (ideally, both) of two directions:
1) Map (say) the first three quires from EVA to your proposed Latin alphabet equivalents and compare the h1 and h2 values to those of the types of texts you mined for vocabulary matches. I understand that there are some ambiguities in doing this -- should EVA 'ar' be read as "N" or "AR", for example -- but you have to start somewhere.
2) Take some of the contemporary texts you mined for vocabulary and convert them to EVA using your scheme (or better yet, to the Currier transcription alphabet, which also groups some of the oversplit EVA sequences to single characters) -- does doing that lower h1 & h2 to levels consistent with the Voynich text? There may be some difficulties automating this perfectly, but again you have to start somewhere.