On the Road to Improved Lexical Confusability Metrics

Pronounciation modeling in automatic speech recognition systems has had mixed results in the past; one likely reason for poor performance is the increased confusability in the lexicom from adding new pronounciation variants. In this work, we propose a new framework for determining lexically confusable words based on inverted finite state transducers (FSTs); we also present some initial experiments designed to test some of the implementation details of this framework.