We consider the problem of predicting the surface pronunciations of a word in conversational speech, using a feature-based model of pronunciation variation. We build context-dependent decision trees for both phone-based and feature-based models, and compare their perplexities on conversational data from the Switchboard Transcription Project. We find that feature-based decision trees using featur e bundles based on articulatory phonology outperform phone-based decision trees, and are much more r obust to reductions in training data. We also analyze the usefulness of various context variables.
Bibliographic reference. Bowman, Sam / Livescu, Karen (2010): "Modeling pronunciation variation with context-dependent articulatory feature decision trees", In INTERSPEECH-2010, 326-329.