Efficient rule scoring for improved grapheme-based lexicons

For many languages, an expert-defined phonetic lexicon may not exist. One popular alternative is the use of a grapheme-based lexicon. However, there may be a significant difference between the orthography and the pronunciation of the language. In our previous work, we proposed a statistical machine translation based approach to improving grapheme-based pronunciations. Without knowledge of true target pronunciations, a phrase table was created where each individual rule improved the likelihood of the training data when applied. The approach improved recognition accuracy, but required significant computational cost. In this work, we propose an improvement that increases the speed of the process by more than 80 times without decreasing recognition accuracy.