French large vocabulary recognition with cross-word phonology transducers

Although finite-state transducers have been widely used in linguistics, their application to speech recognition has begun only recently (M. Mohri, 1997). We describe our implementation of French large vocabulary recognition based on transducers, and how we take advantage of this approach to integrate automatic pronunciation rules and cross-word phenomena such as French "liaison". We also show that a simple, single-level Viterbi algorithm can efficiently decode speech recognition transducers and handle cross-word context models and cross-word phonological rules. In our experiments we compared network size, error rate and decoding speed of our transducer based recognizer against a baseline HTK recognizer, on a large vocabulary French dictation task. Transducers reduced search time by a factor of 25 compared to our HTK recognizer. We also examined the effect of automated pronunciation rules, and their combination with crossword phonological rules that control "liaison". We obtained a 23% relative reduction in the word error rate on a 5000 word task.