Paper: Applying Many-to-Many Alignments and Hidden Markov Models to Letter-to-Phoneme Conversion

ACL ID N07-1047
Title Applying Many-to-Many Alignments and Hidden Markov Models to Letter-to-Phoneme Conversion
Venue Human Language Technologies
Session Main Conference
Year 2007
Authors

Letter-to-phoneme conversion generally requires aligned training data of letters and phonemes. Typically, the align- ments are limited to one-to-one align- ments. We present a novel technique of training with many-to-many alignments. A letter chunking bigram prediction man- ages double letters and double phonemes automatically as opposed to preprocess- ing with fixed lists. We also apply an HMM method in conjunction with a local classification model to predict a global phoneme sequence given a word. The many-to-many alignments result in significant improvements over the tradi- tional one-to-one approach. Our system achieves state-of-the-art performance on several languages and data sets.