The efficient generation of pronunciation dictionaries: machine learning factors during bootstrapping

Several factors affect the efficiency of bootstrapping approaches to the generation of pronunciation dictionaries. We focus on factors related to the underlying rule-extraction algorithms, and demonstrate variants of the Dynamically Expanding Context algorithm, which are beneficial for this application. In particular, we show that continuous updating of the learned rules, coupled with a new approach to grapheme-to-phoneme alignment and a sliding-window approach to choosing the context window, leads to an efficient and accurate bootstrapping mechanism.