Weighted finite-state transducer library

OpenFst is a library for constructing, combining, optimizing, and
searching weighted finite-state transducers (FSTs).  Weighted
finite-state transducers are automata where each transition has an input
label, an output label, and a weight.  The more familiar finite-state
acceptor is represented as a transducer with each transition's input and
output label equal.  Finite-state acceptors are used to represent sets
of strings (specifically, regular or rational sets); finite-state
transducers are used to represent binary relations between pairs of
strings (specifically, rational transductions).  The weights can be used
to represent the cost of taking a particular transition.

FSTs have key applications in speech recognition and synthesis, machine
translation, optical character recognition, pattern matching, string
processing, machine learning, information extraction and retrieval among
others.  Often a weighted transducer is used to represent a
probabilistic model (e.g., an n-gram model, pronunciation model).  FSTs
can be optimized by determinization and minimization, models can be
applied to hypothesis sets (also represented as automata) or cascaded by
finite-state composition, and the best results can be selected by
shortest-path algorithms.