Abstract
This chapter defines a Discrete-Time and Continuous-Time Markov
Chain Process aimed to identify the language used to write a text. This is a brief
introduction to show the usefulness of both random walks in the recognition of a
language, and how these methods can lead to deepen the recognition using other
possible structural language. An example is established and solved from diphthongs
of the English language
Keywords: Conditional Probabilities, Continuous-Time Markov Chain Process, Discrete-Time Markov Chain Process, Discrete-Time and Continuous-Time Markov Chain Process, English Diphthongs, Initial State Vector, Matrix of Transition Probabilities, Natural Language Recognition, Steady-State Vector, Transition Matrix
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