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Using Hidden Markov Chains in Recognition of Vowel Letters in English Language | ||
| IRAQI JOURNAL OF STATISTICAL SCIENCES | ||
| Article 8, Volume 6, Issue 1, 2006, Pages 67-91 PDF (0 K) | ||
| DOI: 10.33899/iqjoss.2006.032725 | ||
| Abstract | ||
| 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 . | ||
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