Hybrid approach to grapheme to phoneme conversion for Korean

In the grapheme to phoneme conversion problem for Korean, two main approaches have been discussed: knowledge-based and data-driven methods. However, both camps have limitations: the knowledge-based hand-written rules cannot handle some of the pronunciation changes due to the lack of capability of linguistic analyzers and many exceptions; data-driven methods always suffer from data sparseness. To overcome the shortages of both camps, this paper presents a novel combining method which effectively integrates two components: (1) a rule-based converting system based on linguistically motivated hand-written rules and (2) a statistical converting system using a Maximum Entropy model. The experimental results clearly show the effectiveness of our proposed method.