A rational design for a weighted finite-state transducer library

We describe the design principles and main algorithms for an object-oriented library for weighted finite-state transducers, which are finite automata in which each transition has an output and a weight as well as the more familiar input. The main goal of the library is to provide algorithms and representations for all the symbolic processing components (language models, dictionaries, acoustic realization rules, word lattices) in large-vocabulary speech recognition systems. This goal leads to several requirements: generality, to support the representation and use of the various information sources in speech recognition; modularity, to allow rapid experimentation with different representations of speech recognition tasks; and efficiency, to support competitive large-vocabulary recognition. Rational power series provide the theoretical foundation for the library by giving the semantics for the objects and operations in the library and by creating the opportunity for optimizations (on-demand composition, determinization and minimization) that are not available in more "ad hoc" speech recognition frameworks. The generality of the library has made it also valuable in other language-processing applications, such as word segmentation for Chinese text [25].