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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. ok jeremy@
27 lines
1.5 KiB
Plaintext
27 lines
1.5 KiB
Plaintext
OpenFst is a library for constructing, combining, optimizing, and
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searching weighted finite-state transducers (FSTs). Weighted
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finite-state transducers are automata where each transition has an input
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label, an output label, and a weight. The more familiar finite-state
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acceptor is represented as a transducer with each transition's input and
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output label equal. Finite-state acceptors are used to represent sets of
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strings (specifically, regular or rational sets); finite-state
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transducers are used to represent binary relations between pairs of
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strings (specifically, rational transductions). The weights can be used
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to represent the cost of taking 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 among
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others. Often a weighted transducer is used to represent a probabilistic
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model (e.g., an n-gram model, pronunciation model). FSTs can be
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optimized by determinization and minimization, models can be applied to
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hypothesis sets (also represented as automata) or cascaded by
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finite-state composition, and the best results can be selected by
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shortest-path algorithms.
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This library was developed by contributors from Google Research and
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NYU's Courant Institute. It is intended to be comprehensive, flexible,
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efficient and scale well to large problems. It has been extensively
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tested. It is an open source project distributed under the Apache
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license.
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