Syllable-based Myanmar Language Model for Speech Recognition

In this paper, we describe the work developed in the creation of syllable-based language model for continuous speech recognition system for Myanmar language. Speech recognition systems contain language model as the part of prediction word order sequence. In English and other languages, speech recognition system can use word-based language model. Since Myanmar is monosyllabic and syllable-timed language, the syllable-based language model is more suitable in speech recognition system. At the first step, this paper explains the structure of traditional (Phrase based) language model and describes how to normalized Myanmar sentences and building of normalized Myanmar language model. In the second step, the evaluation of the two language models is expressed. Finally, the comparison of these two language models with the perplexity values is described. The syllable-based Myanmar language model has lower perplexity value than the traditional language model. This language model can support the creation of word-based Myanmar language model.