Applying Linguistic G2P Knowledge on a Statistical Grapheme-to-phoneme Conversion in Khmer

Abstract

A Grapheme-to-Phoneme (G2P) convertor generates the pronunciation given a word. G2P is an important module in a speech synthesis system and an automatic speech recognition system. Two main G2P approaches are: knowledge-based and data-driven. The knowledge based G2P is built based on linguist knowledge. The data-driven approach such as the statistical approach on the other hand does not need expert knowledge, but it requires data to learn the rules. In this research, we propose an approach that combines linguistic knowledge into a statistical-based G2P convertor for Khmer. We examined a simple way of adding linguistic knowledge into the statistical G2P convertor by simply inserting vowel tags into a Khmer word. Three types of vowel tags were used. The main strength of this approach is it combines the strength of linguistic knowledge and statistical-based approach, to build a robust G2P model. The information allows better modeling and prediction of the phoneme sequence, thus improving the phoneme error rate (PER) and word error rate (WER). The PER and WER of our proposed Khmer G2P improve from 23.2% and 69.6% to 11.1% and 51.4% respectively.