A Chunk-based n-gram English to Thai Transliteration

Abstract

In this study, a chunk-based n-gram model is proposed for English to Thai transliteration. The model is compared with three other models: table lookup model, decision tree model, and statistical model. The chunk-based n-gram model achieves 67% word accuracy, which is higher than the accuracy of other models. Performances of all models are slightly increased when an English grapheme to phoneme is included in the system. However, the accuracy of the system does not suffice to be a public transliteration tool. The low accuracy of the system is caused by the poor performance of the English grapheme to phoneme module and the inconsistency of pronunciation in the training data. Some suggestions are provided for further improvement.