Multi-level decision trees for static and dynamic pronunciation models
Eric Fosler‐Lussier · 1999
We have been focusing on improving pronunciation models for automatic transcription of television and radio news reports by modeling phone, syllable, and word pronunciation distributions with decision trees. These models were employed in two separate sets of experiments. First, decision trees facilitated selection of word pronunciations derived automatically from data for use in a standard speech recognizer dictionary. We have seen a small but significant improvement with these automatically constructed dictionaries in our one-pass decoding system. In a second set of experiments, we allowed decision tree models to determine the probability of word pronunciations dynamically, dependent on the linguistic context of the word during recognition. Dynamic models provided an additional insignificant decrease in error, but improvements were focused within the spontaneous speech portion of the test set. 1. INTRODUCTION One goal of recent research within the ASR community has been to provide s...