Weighted determinization and minimization for large vocabulary speech recognition
Mehryar Mohri, Michael Riley · 1997
Speech recognition requires solving many space and time problems that can have a critical effect on the overall system performance. We describe the use of two general new algorithms [5] that transform recognition networks into equivalent ones that require much less time and space in large-vocabulary speech recognition. The new algorithms generalize classical automata determinization and minimization to deal properly with the probabilities of alternative hypotheses and with the relationships between units (distributions, phones, words) at different levels in the recognition system. 1. INTRODUCTION The networks used in the search stage of speech recognition systems are often highly redundant. Many paths correspond to the same word contents (word lattices and language models), or to the same phonemes (pronunciation dictionaries) for instance, with distinct weights or probabilities. More generally, at a given state of a network there might be several thousand alternative outgoing arcs, ma...