OpenFst is a library for constructing, combining, optimizing, and searching weighted finitestate transducers (FSTs). Weighted finitestate transducers are automata where each transition has an input label, an output label, and a weight. The more familiar finitestate acceptor is represented as a transducer with each transition's input and output label equal. Finitestate acceptors are used to represent sets of strings (specifically, regular or rational sets); finitestate 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 ngram 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 finitestate composition, and the best results can be selected by shortestpath algorithms. This library was developed by contributors from Google Research and NYU's Courant Institute. It is intended to be comprehensive, flexible, efficient and scale well to large problems. It has been extensively tested. It is an open source project distributed under the Apache license

OpenFst Library
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