A Hidden Markov Model for the Linguistic Analysis of the Voynich Manuscript

Abstract

Hidden Markov models are a very useful tool in the modeling of time series and any sequence of data. In particular, they have been successfully applied to the field of mathematical linguistics. In this paper, we apply a hidden Markov model to analyze the underlying structure of an ancient and complex manuscript, known as the Voynich manuscript, which remains undeciphered. By assuming a certain number of internal states representations for the symbols of the manuscripts, we train the network by means of the α and β -pass algorithms to optimize the model. By this procedure, we are able to obtain the so-called transition and observation matrices to compare with known languages concerning the frequency of consonant andvowel sounds. From this analysis, we conclude that transitions occur between the two states with similar frequencies to other languages. Moreover, the identification of the vowel and consonant sounds matches some previous tentative bottom-up approaches to decode the manuscript.

Evaluating the CDMA System Using Hidden Markov and Semi Hidden Markov Models

“…These models are clear with facile description and can be merged into larger models; moreover, the algorithms for manipulating them are simply applied. Hidden Markov modeling has emerged to be profitable in a number of applications such as speech recognition [1,2] , biology [3] , gesture recognition [4,5] , text processing [6] , biochemistry [7] , electrocardiographic [8] , econometrics [9] , financial stock prediction [10] , signal processing [11] , bioinformatics and genomics [12,13] , machine translation [14] and road sign detection [15] .…”