Using Hidden Markov Chains in Recognition of Vowel Letters in English Language

This study deals with hidden Markov models. These models consist of sets of finite states, each one of them is associated with a probability distribution. The transition among the states is governed by a set of probabilities namely “ Transition probabilities ”. In general, the final observation produced according to the associated probability, where there is only probability production instead of a clear visible states. Therefore, these states are described as hidden The basic problems for hidden Markov models (HMMs) are: - The probability account for the observation sequence (O) when the model is given = (A, B, ), i.e. P(O|) Where: A = The state transition probabilities. B = The observation probability matrix. The initial state distribution. - Choosing the optimal state of the sequence for hidden Markov process. - Finding the model  = (A, B, ) which has the greatest probability, i.e. re-estimating the model to maximiz P(O|). in addition to its results.

Abstract licence: CC BY

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