Statistical modeling of phonological rules through linguistic hierarchies

This paper describes our research aimed at acquiring a generalized probability model for alternative phonetic realizations in conversational speech. For all of our experiments, we utilize the summit landmark-based speech recognition framework. The approach begins with a set of formal context-dependent phonological rules, applied to the baseforms in the recognizer's lexicon. A large speech corpus is phonetically aligned using a forced recognition procedure. The probability model is acquired by observing specific realizations expressed in these alignments. A set of context-free rules is used to parse words into substructure, in order to generalize context-dependent probabilities to other words that share the same sub-word context. The model maps phones to sub-word units probabilistically in a finite state transducer framework, capturing phonetic predictions based on local phonemic, morphologic, and syllabic contexts. We experimented within two domains: the mercury flight reservation domain and the jupiter weather domain. The baseline system used the same set of phonological rules for lexical expansion, but with no probabilities for the alternates. We achieved 14.4% relative reduction in concept error rate for jupiter and 16.5% for mercury.