Abstract

We explore different ways of "spelling" a word in a speech recognizer's lexicon and how to obtain those spellings. In particular, we compare using as the source of sub-words units for which we build acoustic models (1) a coarse phonemic representation, (2) a single, fine phonetic realization, and (3) multiple phonetic realizations with associated likelihoods. We describe how we obtain these different pronunciations from text-to-speech systems and from procedures that build decision trees trained on phonetically-labeled corpora. We evaluate these methods applied to speech recognition with the DARPA Resource Management (RM) and the North American Business News (NAB) tasks. For the RM task (with perplexity 60 grammar), we obtain 93.4% word accuracy using phonemic pronunciations, 94.1% using a single phonetic pronunciation per word, and 96.3% using multiple phonetic pronunciations per word with associated likelihoods. For the NAB task (with 60K vocabulary and 34M 1-5 grams), we obtain 87.3% word accuracy with phonemic pronunciations and 90.0% using multiple phonetic pronunciations

Citations