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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.
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