A Transformation-Based Learning Method on Generating Korean Standard Pronunciation
Dong-Sung Kim, Roh Chang-hwa · Institutional Repositories DataBase (IRDB) · 2007
In this paper, we propose a Transformation-Based Learning (TBL) method on generating the Korean standard pronunciation.Previous studies on the phonological processing have been focused on the phonological rule applications and the finite state automata (Johnson 1984;Kaplan and Kay 1994;Koskenniemi 1983;Bird 1995).In case of Korean computational phonology, some former researches have approached the phonological rule based pronunciation generation system (Lee et al. 2005;Lee 1998).This study suggests a corpus-based and data-oriented rule learning method on generating Korean standard pronunciation.In order to substituting rule-based generation with corpusbased one, an aligned corpus between an input and its pronunciation counterpart has been devised.We conducted an experiment on generating the standard pronunciation with the TBL algorithm, based on this aligned corpus.