Generalized optimization algorithm for speech recognition transducers

Weighted transducers provide a common representation for the components of a speech recognition system. In previous work, we showed that these components can be combined off-line into a single compact recognition transducer that maps directly HMM state sequences to word sequences. The construction of that recognition transducer and its efficiency of use critically depend on the use of a general optimization algorithm, determinization. However, not all weighted automata and transducers used in large-vocabulary speech recognition are determinizable. We present a general algorithm that can make an arbitrary weighted transducer determinizable and generalize our previous optimization technique for building an integrated recognition transducer to deal with arbitrary weighted transducers used in speech recognition. We report experimental results in a large- vocabulary speech recognition task, How May I Help You (HMIHY), showing that our generalized technique leads to a recognition transducer that performs as well as our original solution in the case of classical n-gram models while inserting less special symbols, and that it leads to a substantial improvement of the recognition speed, factor of 2.6, in the same task when using a class-based language model.