Phonological parsing for reversible letter-to-sound/sound-to-letter generation

In this paper, we describe a reversible letter-to-sound/sound-to-letter generation system based on an approach which combines a rule-based formalism with data-driven techniques. We adopt a probabilistic parsing strategy to provide a hierarchical lexical analysis of a word, including information such as morphology, stress, syllabification, phonemics and graphemics. Long-distance constraints are propagated by enforcing local constraints throughout the hierarchy. Our training and testing corpora are derived from the high-frequency portion of the Brown Corpus (10,000 words), augmented with markers indicating stress and word morphology. We evaluated our performance on both letter-to-sound and sound-to-letter generation in terms of whole word accuracy and phoneme/letter accuracy. Using 26 letters and 52 phonemes with an unseen test set, we achieved a word accuracy of 69.3% and a phoneme accuracy of 91.7% for letter-to-sound generation, and a word accuracy of 51.9% and letter accuracy of 88.6% for sound-to-letter generation