Adaptive Mixtures of Probabilistic Transducers
Neural Computation
Hybrid statistical pronunciation models designed to be trained by a medium-size corpus
Computer Speech and Language
Extracting phoneme pronunciation information from corpora
NeMLaP3/CoNLL '98 Proceedings of the Joint Conferences on New Methods in Language Processing and Computational Natural Language Learning
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Methods to predict detailed phonetic pronunciations from a coarse phonemic transcription are described. The phonemic base forms, obtainable from orthographic text by dictionary lookup and other means, do not specify fine phonetic detail such as flapping, glottal stop insertion, or the formation of syllabic nasals and liquids. These phenomena depend on the phonetic context (often spanning word boundaries), stress environment, speaking rate, and dialect. A procedure is presented that builds decision trees, trained on the TIMIT database, using some of these features to predict pronunciation alternatives. The resulting phonetic network predicts the correct pronunciation of a phoneme on test data from the same corpus approximately 83% of the time and the correct phone was in the top five guesses 99% of the time.