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    Transliteration system using pair HMM with weighted FSTs

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    Book chapter (113.1Kb)
    Date
    2009-08-07
    Author
    Nabende, Peter
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    Abstract
    This paper presents a transliteration system based on pair Hidden Markov Model (pair HMM) training and weighted Finite State Transducer (WFST)techniques. Parameters used by WFSTs for transliteration generation are learned from a pair HMM. Parameters from pair HMM training on English-Russian data sets are found to give better transliteration quality than parameters trained for WFSTs for corresponding structures. Training a pair HMM on English vowel bigrams and standard bigrams for Cyrillic Romanization and using a few transformation rules on generated Russian transliterations to test for context improves the system's transliteration quality.
    URI
    http://hdl.handle.net/10570/380
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