[Bug 681976] New: Review Request: openfst - weighted finite-state transducer library

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Summary: Review Request: openfst - weighted finite-state transducer library

https://bugzilla.redhat.com/show_bug.cgi?id=681976

           Summary: Review Request: openfst - weighted finite-state
                    transducer library
           Product: Fedora
           Version: rawhide
          Platform: All
        OS/Version: Linux
            Status: NEW
          Severity: medium
          Priority: medium
         Component: Package Review
        AssignedTo: nobody at fedoraproject.org
        ReportedBy: loganjerry at gmail.com
         QAContact: extras-qa at fedoraproject.org
                CC: notting at redhat.com, fedora-package-review at redhat.com
   Estimated Hours: 0.0
    Classification: Fedora


Spec URL: http://jjames.fedorapeople.org/openfst/openfst.spec
SRPM URL: http://jjames.fedorapeople.org/openfst/openfst-1.2.7-1.fc14.src.rpm
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.

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