Grapheme-to-phone using finite-state transducers

Several approaches have been adopted over the years for grapheme-to-phone conversion for European Portuguese: hand-derived rules, neural networks, classification and regression trees, etc. This paper describes different approaches implemented as weighted finite state transducers (WFST), motivated by their flexibility in integrating multiples sources of information and other interesting properties such as inversion. We describe and compare rule-based, data-driven and hybrid approaches. Best results were obtained with the rule-based approach, but one should take into account the fact that the data-driven one was trained with automatically transcribed material.