Hybrid Grapheme to Phoneme Conversion forUnlimited

Both dictionary-based and rule-based methods on grapheme-to-phoneme conversion have their own advantages and limitations. For example, a large sized phonetic dictionary and complex morphophonemic rules are required for the dictionary-based method and the LTS(letter to sound) rule-based method itself cannot model the complete morphophonemic constraints. This paper describes a grapheme-to-phoneme conversion method using a dictionary-based and rule-based hybrid method with a phonetic pattern dictionary and CCV (consonant consonant vowel) LTS (letter to sound) rules. The phonetic pattern dictionary, standing for the dictionary-based method, contains entries in the form of a morpheme pattern and its phonetic pattern. The patterns represent candidate phonological changes in left and right boundaries of morphemes. Obviously, the CCV LTS rules stands for the rule-based method. The rules are in charge of grapheme-to-phoneme conversion within morphemes. The conversion method consists of mainly two steps including morpheme to phoneme conversion and morphophonemic connectivity check, and two preprocessing steps including phrase break detection and morpheme normalization. Phrase break detection presumes phrase breaks using the stochastic method on part-of-speech (POS) information. Morpheme normalization is to replace non-Korean symbols with their corresponding standard Korean graphemes. In the morpheme phoneticizing module, each morpheme in the phrase is converted into phonetic patterns by looking it up in the phonetic pattern dictionary. Graphemes within a morpheme are grouped into CCV units and converted into phonemes by the CCV LTS rules. The morphophonemic connectivity table supports grammaticality checking of the two adjacent phonetic morphemes. In experiments with a non-Korean symbol free corpus of 4,973 sentences, we achieved a 99.98% grapheme-to-phoneme conversion performance rate and a 99.0% sentence conversion performance rate. With a news broadcast corpus of 621 sentences, 99.7% of the graphemes and 86.6% of the sentences are correctly converted. The full Korean TTS (Text-to-Speech) system is now being implemented using this conversion method.