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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.
21 lines
1.3 KiB
Plaintext
21 lines
1.3 KiB
Plaintext
OpenFst is a library for constructing, combining, optimizing, and searching
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weighted finite-state transducers (FSTs). Weighted finite-state transducers
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are automata where each transition has an input label, an output label, and
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a weight. The more familiar finite-state acceptor is represented as a
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transducer with each transition's input and output label equal.
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Finite-state acceptors are used to represent sets of strings (specifically,
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regular or rational sets); finite-state transducers are used to represent
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binary relations between pairs of strings (specifically, rational
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transductions). The weights can be used to represent the cost of taking
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a particular transition.
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FSTs have key applications in speech recognition and synthesis, machine
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translation, optical character recognition, pattern matching, string
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processing, machine learning, information extraction and retrieval
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among others. Often a weighted transducer is used to represent a
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probabilistic model (e.g., an n-gram model, pronunciation model). FSTs can
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be optimized by determinization and minimization, models can be applied to
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hypothesis sets (also represented as automata) or cascaded by finite-state
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composition, and the best results can be selected by shortest-path algorithms.
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WWW: http://www.openfst.org/
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