Transliteration system using pair HMM with weighted FSTs
Peter Nabende · 2009
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.