Description:

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.