In this paper we apply a framework of finite-state transducers (FST) to uniformly represent various information sources and data-structures used in speech recognition. These source models include context-free language models, phonology models, acoustic model information (Hidden Markov Models), and pronunciation dictionaries. We will describe how this unified representation can serve as a single input model for the recognizer. We will demonstrate how the application of various levels of optimizations can lead to a more compact representation of these transducers and evaluate the effects on recognition performance, in terms of accuracy and computational complexity.